From 4dc8456f22350bfdfe78f3f9a5261be648ffe9a8 Mon Sep 17 00:00:00 2001 From: Tyler Bettilyon Date: Fri, 17 Apr 2020 23:22:36 -0600 Subject: [PATCH] updated transfer learning with discriminative learning rates section --- .../03-transfer-learning.ipynb | 760 +++++++++++++++--- 1 file changed, 655 insertions(+), 105 deletions(-) diff --git a/04-convolutional-neural-networks/03-transfer-learning.ipynb b/04-convolutional-neural-networks/03-transfer-learning.ipynb index 865c40f4..9ec5f833 100644 --- a/04-convolutional-neural-networks/03-transfer-learning.ipynb +++ b/04-convolutional-neural-networks/03-transfer-learning.ipynb @@ -26,6 +26,7 @@ "from tensorflow.keras.models import Model, Sequential\n", "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", "from tensorflow.keras.utils import to_categorical\n", + "from tensorflow.keras.optimizers import SGD\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy import ndimage\n", @@ -36,11 +37,9 @@ "from tensorflow.keras.datasets import cifar100\n", "(x_train, y_train), (x_test, y_test) = cifar100.load_data(label_mode='fine')\n", "\n", - "# Some global factors that will be kept constant. \n", - "batch_size = 32\n", - "num_classes = 100\n", - "image_size = 96\n", - "epochs = 5\n", + "# Constant number of labels, square image shape\n", + "NUM_CLASSES = 100\n", + "IMAGE_SIZE = 96\n", "\n", "# FOR DISPLAY PURPOSES\n", "unprocessed_training_images = x_train\n", @@ -62,8 +61,8 @@ "x_test = np.array([adjust_input_image(x) for x in x_test])\n", "\n", "# And we still need to one-hot encode the labels as usual\n", - "y_train = to_categorical(y_train, num_classes)\n", - "y_test = to_categorical(y_test, num_classes)" + "y_train = to_categorical(y_train, NUM_CLASSES)\n", + "y_test = to_categorical(y_test, NUM_CLASSES)" ] }, { @@ -644,7 +643,7 @@ } ], "source": [ - "base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(96, 96, 3))\n", + "base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3))\n", "\n", "# Layers go from 0-154\n", "# initial=0-9\n", @@ -699,7 +698,7 @@ "# and then repeatedly train on those outputs and the training labels\n", "\n", "# Like before, we grab a pretrained model with include_top=False\n", - "base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(96, 96, 3))\n", + "base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3))\n", "\n", "# Unlike before, we're going to just run the images through this base layer once\n", "# This takes awhile, we're essentially doing a round of evaluation on both datasets.\n", @@ -714,12 +713,59 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 40000 samples, validate on 10000 samples\n", + "Epoch 1/20\n", + "40000/40000 [==============================] - 5s 117us/sample - loss: 2.9527 - accuracy: 0.2856 - val_loss: 2.0693 - val_accuracy: 0.4520\n", + "Epoch 2/20\n", + "40000/40000 [==============================] - 4s 106us/sample - loss: 2.1225 - accuracy: 0.4385 - val_loss: 1.8516 - val_accuracy: 0.5007\n", + "Epoch 3/20\n", + "40000/40000 [==============================] - 4s 104us/sample - loss: 1.8827 - accuracy: 0.4904 - val_loss: 1.7635 - val_accuracy: 0.5180\n", + "Epoch 4/20\n", + "40000/40000 [==============================] - 4s 101us/sample - loss: 1.7279 - accuracy: 0.5248 - val_loss: 1.7331 - val_accuracy: 0.5326\n", + "Epoch 5/20\n", + "40000/40000 [==============================] - 4s 106us/sample - loss: 1.6168 - accuracy: 0.5523 - val_loss: 1.7066 - val_accuracy: 0.5370\n", + "Epoch 6/20\n", + "40000/40000 [==============================] - 4s 107us/sample - loss: 1.5083 - accuracy: 0.5768 - val_loss: 1.6670 - val_accuracy: 0.5501\n", + "Epoch 7/20\n", + "40000/40000 [==============================] - 4s 106us/sample - loss: 1.4191 - accuracy: 0.5996 - val_loss: 1.6489 - val_accuracy: 0.5548\n", + "Epoch 8/20\n", + "40000/40000 [==============================] - 4s 105us/sample - loss: 1.3453 - accuracy: 0.6170 - val_loss: 1.6595 - val_accuracy: 0.5558\n", + "Epoch 9/20\n", + "40000/40000 [==============================] - 4s 106us/sample - loss: 1.2842 - accuracy: 0.6315 - val_loss: 1.6453 - val_accuracy: 0.5614\n", + "Epoch 10/20\n", + "40000/40000 [==============================] - 4s 106us/sample - loss: 1.2286 - accuracy: 0.6437 - val_loss: 1.6556 - val_accuracy: 0.5611\n", + "Epoch 11/20\n", + "40000/40000 [==============================] - 4s 106us/sample - loss: 1.1681 - accuracy: 0.6568 - val_loss: 1.6471 - val_accuracy: 0.5642\n", + "Epoch 12/20\n", + "40000/40000 [==============================] - 4s 106us/sample - loss: 1.1283 - accuracy: 0.6686 - val_loss: 1.6564 - val_accuracy: 0.5611\n", + "Epoch 13/20\n", + "40000/40000 [==============================] - 4s 108us/sample - loss: 1.0588 - accuracy: 0.6850 - val_loss: 1.6890 - val_accuracy: 0.5651\n", + "Epoch 14/20\n", + "40000/40000 [==============================] - 4s 107us/sample - loss: 1.0247 - accuracy: 0.6948 - val_loss: 1.6878 - val_accuracy: 0.5690\n", + "Epoch 15/20\n", + "40000/40000 [==============================] - 4s 106us/sample - loss: 0.9861 - accuracy: 0.7046 - val_loss: 1.7226 - val_accuracy: 0.5606\n", + "Epoch 16/20\n", + "40000/40000 [==============================] - 4s 104us/sample - loss: 0.9494 - accuracy: 0.7144 - val_loss: 1.7383 - val_accuracy: 0.5648\n", + "Epoch 17/20\n", + "40000/40000 [==============================] - 4s 106us/sample - loss: 0.9231 - accuracy: 0.7211 - val_loss: 1.7445 - val_accuracy: 0.5637\n", + "Epoch 18/20\n", + "40000/40000 [==============================] - 4s 107us/sample - loss: 0.8802 - accuracy: 0.7330 - val_loss: 1.7410 - val_accuracy: 0.5600\n", + "Epoch 19/20\n", + "40000/40000 [==============================] - 4s 108us/sample - loss: 0.8551 - accuracy: 0.7365 - val_loss: 1.7681 - val_accuracy: 0.5632\n", + "Epoch 20/20\n", + "40000/40000 [==============================] - 4s 108us/sample - loss: 0.8200 - accuracy: 0.7485 - val_loss: 1.7912 - val_accuracy: 0.5631\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -733,8 +779,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test loss: 1.9\n", - "Test accuracy: 0.558\n" + "Test loss: 1.78\n", + "Test accuracy: 0.566\n" ] } ], @@ -748,11 +794,16 @@ "# features predicted by base_model!\n", "model = Sequential()\n", "model.add(GlobalAveragePooling2D(input_shape=training_features.shape[1:]))\n", - "model.add(Dropout(rate=0.3))\n", - "model.add(Dense(units=100, activation='softmax'))\n", + "\n", + "# Note from the summary, this will result in 1280 nodes, let's use the classic \"squeeze\" and add dropout\n", + "model.add(Dense(units=640, activation='relu'))\n", + "model.add(Dropout(rate=0.4))\n", + "model.add(Dense(units=320, activation='relu'))\n", + "model.add(Dropout(rate=0.2))\n", + "model.add(Dense(units=NUM_CLASSES, activation='softmax'))\n", "\n", "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", - "history = model.fit(training_features, y_train, batch_size=batch_size, epochs=20, validation_split=0.2, verbose=False)\n", + "history = model.fit(training_features, y_train, batch_size=128, epochs=20, validation_split=0.2, verbose=True)\n", "\n", "plot_training_history(history, model)\n", "loss, accuracy = model.evaluate(test_features, y_test, verbose=False)\n", @@ -764,11 +815,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It's worth noting that cifar100 is a very tricky dataset. The images are small, 100 classes is several, and 100 classes amongst rougly 60k examples is only on average 600 samples per class. In fact 28% would have put us in the top 20 on a semi-recent Kaggle competition, and 50% in the top ten: \n", + "It's worth noting that cifar100 is a very tricky dataset. The images are small, 100 classes is several, and 100 classes amongst rougly 60k examples is only on average 600 samples per class. In fact this 56% test accuracy would have put us in 4th place in a 2016 Kaggle competition. Obviously that's a few years back now... but MobileNetV2 and this 3 layer top isn't exactly \"state of the art\" either. It only took about 3 minutes to train this network. \n", "\n", "https://www.kaggle.com/c/ml2016-7-cifar-100/leaderboard\n", "\n", - "Boom, we're a top ten Kaggel competitor just like that. Though, looking at validation accuracy and loss, it's not clear we're going to improve much further. It may be worth trying to do transfer learning from a different pre-trained model." + "Boom, we're a top ten Kaggel competitor just like that. Though, looking at validation accuracy and loss, it's not clear we're going to improve much further, and clearly even with significant dropout we're overfitting. It may be worth trying to do transfer learning from a different pre-trained model." ] }, { @@ -786,7 +837,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "metadata": { "scrolled": false }, @@ -800,13 +851,19 @@ "# The later layers have learned combinations of those features and are increasingly specific to the task\n", "# that the network was trained for. \n", "\n", - "def transfer_from_mobilenet(optimizer, freeze_first_n_layers):\n", - " base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(96, 96, 3))\n", "\n", + "def transfer_from_mobilenet(optimizer, freeze_first_n_layers, batch_size, epochs):\n", + " base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3))\n", + " \n", + " # We'll use the same \"squeeze\" w/ dropout structure from above...\n", + " # We have to use the \"functional\" API now, which is why we're not using model.add\n", " old_top = base_model.output\n", " old_top = GlobalAveragePooling2D()(old_top)\n", + " old_top = Dense(units=640, activation='relu')(old_top)\n", + " old_top = Dropout(rate=0.4)(old_top)\n", + " old_top = Dense(units=320, activation='relu')(old_top)\n", " old_top = Dropout(rate=0.2)(old_top)\n", - " new_top = Dense(num_classes, activation='softmax')(old_top)\n", + " new_top = Dense(NUM_CLASSES, activation='softmax')(old_top)\n", " \n", " model = Model(inputs=base_model.input, outputs=new_top)\n", "\n", @@ -822,7 +879,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "metadata": { "scrolled": false }, @@ -835,20 +892,20 @@ "\n", "Train on 40000 samples, validate on 10000 samples\n", "Epoch 1/5\n", - "40000/40000 [==============================] - 750s 19ms/sample - loss: 2.1506 - accuracy: 0.4571 - val_loss: 3.1641 - val_accuracy: 0.2728\n", + "40000/40000 [==============================] - 331s 8ms/sample - loss: 4.2653 - accuracy: 0.0844 - val_loss: 4.0703 - val_accuracy: 0.1139\n", "Epoch 2/5\n", - "40000/40000 [==============================] - 619s 15ms/sample - loss: 1.2955 - accuracy: 0.6364 - val_loss: 3.1304 - val_accuracy: 0.2892\n", + "40000/40000 [==============================] - 319s 8ms/sample - loss: 2.8076 - accuracy: 0.3186 - val_loss: 3.2642 - val_accuracy: 0.2404\n", "Epoch 3/5\n", - "40000/40000 [==============================] - 580s 14ms/sample - loss: 1.0442 - accuracy: 0.7003 - val_loss: 3.0216 - val_accuracy: 0.2920\n", + "40000/40000 [==============================] - 316s 8ms/sample - loss: 2.1025 - accuracy: 0.4525 - val_loss: 3.1112 - val_accuracy: 0.2640\n", "Epoch 4/5\n", - "40000/40000 [==============================] - 586s 15ms/sample - loss: 0.8732 - accuracy: 0.7443 - val_loss: 3.0917 - val_accuracy: 0.3077\n", + "40000/40000 [==============================] - 306s 8ms/sample - loss: 1.7899 - accuracy: 0.5204 - val_loss: 3.0310 - val_accuracy: 0.2856\n", "Epoch 5/5\n", - "40000/40000 [==============================] - 646s 16ms/sample - loss: 0.7349 - accuracy: 0.7820 - val_loss: 3.1008 - val_accuracy: 0.3039\n" + "40000/40000 [==============================] - 310s 8ms/sample - loss: 1.5907 - accuracy: 0.5649 - val_loss: 3.0163 - val_accuracy: 0.2860\n" ] }, { "data": { - "image/png": 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xhDEhywLK1IuDpRW8v3ALY2fnsX1/CV1aJfH8qN5c1qM1UZHhfbPh6RKRVKDcDad4YCjORRC+JgE3A3OBkcAMO/9kQp0FlAmoPcWlvD1nI2/P3cT+w+WcndWMP1zdg0GdUpEQ7oKlnrUG3nbPQ0UA41X1XyLyJJCrqpOAN4C/i8g6oBAY5V25xtQNCygTEFsKDzF2dh4f5G6htKKKi7u15K4LsumT0dTr0kKOqi4F+tQy/3GfxyXANfVZlzGBZgFl6tTK7Qd4bdZ6/rl0OxECV/Vpy53nZzeYm2qNMXXHAsqcNlVl/oZCXp21npmrd9EoJpLbBmZy+7ntadUkSHqINsaEHAsoc8qqqpSpK3fy6qz1fLN5HymJMTwyrDM3nt2OJgnRXpdnjAlxFlDmpJVVVPHJN1t57av1rN91kIxmCfz+R2dwTd+0htgNkTEmQCygjN+KSysYN38zb3y9gR0HSujWujEvju7DJWe0skvFjTF1zgLKnNCuolLemrOBv8/dxIGSCgZkN+dPI3tyXscUu1TcGBMwFlDmmDbvOcTrs9fzYW4+ZZVVDO/eirsuyKZXerLXpRljGgALKPMDy7ft59VZeXy2dBtRERH8uG9b7jivfYPuUdwYU/8soAzgXCo+d/0eXpm1ntlrd5MUG8Ud57fn9oFZtGhsl4obY+pfQANKRIYDzwORwFhV/WON5c8BF7pPE4AWqmrHj+pRZZXyxfIdvDprPUvy95OSGMsvh3fhhnMyaBxnl4obY7wTsIBy+w17Gadjy3xgoYhMUtUV1euo6oM+6/+cWrpzMYFRWlHJR4u38rev8sjbfZDM5gn84aoeXH1mW7tU3BgTFALZguoHrFPVPAAReR9n1M8Vx1h/NPDbANZjgJLySt6es5E3vt5AQVEpPdo24a83nMmw7q2IjLAr8owxwSOQAfX9CJ+ufODs2lYUkXZAFjDjGMttFNA6sDR/H2PGL2FtQTHndUzhuet6MyC7uV0qbowJSsFykcQoYII7augP2Cigp6esoooXZ6zlrzPXk5oYy9u39eOCTqlel2WMMccVyICqHuGzWpo7rzajgHsCWEuDtWLbAcZ8uISV2w8wsm8av7m8G03i7eIHY0zwC2RALQQ6ikgWTjCNAq6vuZKIdAGa4owEaupIRWUVr8xczwsz1pKcEMPY/8phSLeWXpdljDF+C1hAqWqFiNwLTMG5zPxNVV1eYxRQcILrfRueuu6s3VnEmA+XsDR/PyN6teF3I7rTtFGM12UZY8xJCeg5KFWdDEyuMe/xGs+fCGQNDUlllTJ2dh5/nrqGxNgo/nrDmVzao7XXZRljzCkJloskzGnasPsgD3+4hEWb9jKse0v+31U9SEmM9bosY4w5ZRZQIa6qSnl77kae+nwVsVGRPD+qNyN6tbFLx40xIc8CKoRtKTzEIxOWMC+vkMFdWvC/V/egpfWbZ4wJExZQIUhVeW/BZv7w2UpEhD+N7Mk1fdOs1WSMCSsWUCFm277D/HLiUmav3c25HVJ4amRP2ibHe12WMcbUOQuoEKGqTFiUz5P/XEGlKr//0RnceHaGtZqMMWHLAioEFBwo4bGPvmP6qgL6ZTXjmZG9yGie4HVZxhgTUBZQQUxVmbRkG49/upyS8koev7wbtwzIJMJ6HTfGNAAWUEFqd3Epv/lkGf9etoM+Gck8c00vsm3IdWNMA2IBFYQ+X7ad//54GUUlFfxyeBfuPL+9jdVkjGlwLKCCyL5DZfx20nI+/XYbZ7RtzHvX9KZzqySvyzLGGE9YQAWJGat28ujE7yg8WMaDQzpx94XZREdGeF2WMcZ4xgLKYwdKyvn9P1fw4aJ8urRK4s1bzuKMtk28LssYYzxnAeWhr9bs4pcTl1JQVMq9F3bgvos6EhNlrSZjjAELKE8Ul1bwh8kreW/+ZrJTGzHxZwPonZ7sdVkmCIlIOvAO0BJQ4HVVfb7GOoOAT4EN7qyPVPXJ+qzTmECwgKpnc9fv4ZEJS9i67zB3nJfFmIs7Excd6XVZJnhVAGNUdbGIJAGLRGSqqq6osd5sVb3cg/qMCRgLqHpyuKySpz5fxVtzNtKueQLjf9qfszKbeV2WCXKquh3Y7j4uEpGVQFugZkAZE3YsoOrBok2FPPzhUjbsPsjN/dvxy0u6kBBj//Tm5IhIJtAHmF/L4v4isgTYBjysqsuPsY07gTsBMjIyAlOoMXXE/koGUEVlFU9/sZq/fZVH6ybxvHfH2QzITvG6LBOCRCQRmAg8oKoHaixeDLRT1WIRuRT4BOhY23ZU9XXgdYCcnBwNYMnGnDa7ZCyA/jFvE6/NyuPanHSmPHi+hZM5JSISjRNO76rqRzWXq+oBVS12H08GokXEftlMyLOACpCDpRW89OU6zmnfjP+9ugeJsdZYNSdPnPFU3gBWquqzx1inlbseItIPZ7/eU39VGhMY9lczQN6as5HdxWW8dlMXG7PJnI6BwE3AdyLyrTvvV0AGgKq+CowEfiYiFcBhYJSq2uE7E/IsoAJg36EyXp21niFdW9C3XVOvyzEhTFW/Bo77DUdVXwJeqp+KjKk/dogvAF6dlUdxaQUPD+vsdSnGGBOyLKDqWMGBEt6as4Ere7WhS6vGXpdjjDEhywKqjr0wYy0VlcqDQzt5XYoxxoQ0C6g6tGnPQd5fsIVR/dJp17yR1+UYY0xIs4CqQ89NXUNUpHDf4FrvkTTGGHMSLKDqyKodB/h0yTZuGZBFi8ZxXpdjjDEhzwKqjjwzZQ2JsVHcdUF7r0sxxpiwENCAEpHhIrJaRNaJyKPHWOdaEVkhIstF5L1A1hMoizbtZdrKnfz0/PYkJ8R4XY4xxoSFgN2oKyKRwMvAUCAfWCgik3zHsRGRjsBjwEBV3SsiLQJVT6CoKk9PWUVKYgy3DszyuhxjjAkbgWxB9QPWqWqeqpYB7wNX1ljnDuBlVd0LoKoFAawnIGav3c28vELuvbADjay/PWOMqTOBDKi2wBaf5/nuPF+dgE4i8h8RmSciwwNYT51zWk+raZscz+izbWwdY4ypS15/5Y/CGbdmEJAGfCUiPVR1n+9KwTrI2r+X7eC7rft55ppexEbZsO3GGFOXAtmC2gqk+zxPc+f5ygcmqWq5qm4A1lDLQGuq+rqq5qhqTmpqasAKPhkVlVU888VqOrZI5Ko+NRuGxhhjTlcgA2oh0FFEskQkBhgFTKqxzic4rSfcAdY6AXkBrKnOfLR4K3m7DjLm4s5ERthwGsYYU9cCFlCqWgHcC0wBVgLjVXW5iDwpIiPc1aYAe0RkBfAl8IiqBv1Aa6UVlfxl2hp6pTVhWPeWXpdjjDFhKaDnoNzhpyfXmPe4z2MFHnKnkPHuvM1s21/Cn0b2ssEIjTEmQKwniZNUXFrBy1+uY0B2c87tmOJ1OcYYE7YsoE7Sm19vYM/BMh6xwQiNMSagLKBOwt6DZfztqzwu7taSPhk2lLsxxgSSXwElIh+JyGUi0qAD7ZVZ6ykus6HcjTGmPvgbOH8FrgfWisgfRaTB/YXesb+Et+ds5Ko+benUMsnrcowxJuz5FVCqOk1VbwDOBDYC00RkjojcKiLRgSwwWDw/fS1Vqjw4xIZyN+GtpLyS5dv2e12GMf6fgxKR5sAtwE+Ab4DncQJrakAqCyIbdx9kfO4WRvfLIL1ZgtflGBNQY8Yv4Zb/W8ihsgqvSzENnL/noD4GZgMJwBWqOkJVP1DVnwOJgSwwGDw7dQ0xkRHcO7iD16UYE3C3nZvJrqJS3pqz0etSTAPnbwvqBVXtpqr/q6rbfReoak4A6goaK7YdYNKSbdw6MJMWSTaUuwl/fds146IuLXh15nr2Hyr3uhzTgPkbUN1EJLn6iYg0FZG7A1RTUHnmi9U0jovip+dne12KMfVmzMWdOVBSwWtfrfe6FNOA+RtQd/gOgeEOMHhHYEoKHrkbC5mxqoC7BmXTJKFBXAtiDADd2jRmRK82/N9/NlJQVOJ1OaaB8jegIsWn0zl3OPeYwJQUHFSVP32+mtSkWG4dYEO5m4bnoaGdKK+s4qUZ67wuxTRQ/gbU58AHInKRiFwEjHPnha2Za3axYGMh9w3uQHyMDUZoGp7MlEZce1Y64xZsZkvhIa/LMQ2QvwH1S5zhMH7mTtOBXwSqKK9VVSlPf76a9GbxXHdW8Izga0x9u29wRyJEeG7qGq9LMQ2QvzfqVqnqK6o60p1eU9XKQBfnlcnLtrNi+wEeHNKJmKgG3buTaeBaNYnjlgGZfPztVlbvKPK6HNPA+HsfVEcRmSAiK0Qkr3oKdHFeqKis4tkv1tCpZSJX9rah3I2564JsEmOieOaL1V6XYhoYf5sH/we8AlQAFwLvAP8IVFFemrAon7zdB3nYhnI3QUJE0kXkS/cL4nIRub+WdUREXhCRdSKyVETOrKv3b9oohjvPb8/UFTtZvHlvXW3WmBPyN6DiVXU6IKq6SVWfAC4LXFneKCmv5Pnpa+mTkczQbjaUu6l7InK/iDR2A+UNEVksIhef4GUVwBhV7QacA9wjIt1qrHMJ0NGd7sT5Qllnbjs3i5TEGJ7+fDXOQNjGBJ6/AVXqDrWxVkTuFZGrCMMujv4xbxPb95fwyLDONpS7CZTbVPUAcDHQFLgJ+OPxXqCq21V1sfu4CFgJ1Dz+fCXwjjrmAcki0rquim4UG8U9F3Zgbt4evl63u642a8xx+RtQ9+P0w3cf0Be4Ebg5UEV5oaiknJe/XMd5HVMYkG1DuZuAqf7mcynwd1Vd7jPvxC8WyQT6APNrLGoLbPF5ns8PQwwRuVNEckUkd9euXSdRNlx/dgZtk+N5eoq1okz9OGFAuTflXqeqxaqar6q3quqP3W9pYWPs7A3sPVRuQ7mbQFskIl/gBNQUEUkCqvx5oYgkAhOBB9xW2ElT1ddVNUdVc1JTU0/qtbFRkTwwpCNL8/czZfmOU3l7Y07KCQPKvZz83HqoxTN7iksZOzuP4d1b0TMt+cQvMObU3Q48CpylqoeAaODWE73IHXdtIvCuqn5UyypbgXSf52nuvDp19ZlpdGiRyDNfrKGyylpRJrD8PcT3jYhMEpGbROTq6imgldWjV2au53B5JQ8Ps8EITcD1B1ar6j4RuRH4NXDc0QHdbsbeAFaq6rPHWG0S8F/uxRfnAPtrjjxQFyIjhIcv7sS6gmI+Wpxf15s35ij+BlQcsAcYDFzhTpcHqqj6tG3fYd6Zt8n9ZmhDuZuAewU4JCK9gDHAepzbNo5nIM7FFINF5Ft3ulRE7hKRu9x1JgN5wDrgb0DARhsY1r0VvdKa8JdpaymtCNv79U0QiPJnJVU94SGIUPXC9LWg8MCQjl6XYhqGClVVEbkSeElV3xCR24/3AlX9mhNcSKHOVQv31GGdxyQiPDKsCze+MZ/35m/m1oHWmbIJDL8CSkT+D/jBAWdVva3OK6pHebuK+XBRPjed0460pjaUu6kXRSLyGE6L6Dz39o2QG8vl3I4pDMhuzksz1nFtTjqNYv36U2LMSfH3EN+/gM/caTrQGCgOVFH15c9T1xAbZUO5m3p1HVCKcz/UDpyLGZ72tqRT88iwzuw5WMabX2/wuhQTpvztLHaiz/QucC0Q0kO9L9u6n8+Wbuf2c7NISYz1uhzTQLih9C7QREQuB0pU9UTnoIJSn4ymXNytJa9/lcfeg2Vel2PC0Kl21d0RaFGXhdS3p6espkl8NHec397rUkwDIiLXAguAa3C+6M0XkZHeVnXqHh7WmeKyCl6dZUPDm7rn7zmoIo4+B7UDZ4yokDQ/bw+z1uzi0Uu60Dgu5A7/m9D23zj3QBUAiEgqMA2Y4GlVp6hTyySu6tOWt+Zs5NaBWbRqEud1SSaM+HuIL0lVG/tMnVR1YqCLCwRV5ekpq2mRFMvN/TO9Lsc0PBHV4eTaw6kfyQgKDw7pRJUqL8xY63UpJsz4Ox7UVSLSxOd5soj8yI/XDReR1e4QAI/WsvwWEdnlc2/HT06u/JP35eoCcjft5b6LOtpQ7sYLn4vIFPd3/xacC48me1zTaUlvlsDofhmMX7iFjbsPel2OCSP+fnP7rap+f7e7qu4Dfnu8F7h9+L2MMwxAN2B0LUMEAHygqr3daayf9ZySqirl6SlraNc8gevOSijztTsAABkUSURBVD/xC4ypY6r6CPA60NOdXlfVkD1cXu3ewR2IjozgWRsa3tQhfwOqtvVOdP6qH7BOVfNUtQx4H2dIAM/8c+k2Vm4/wENDOxEdGdJHVUwIc6+GfcidPva6nrrQIimOWwdmMmnJNlZsO6V+bI35AX//SueKyLMiku1OzwKLTvAav7r/B37sjgA6QURqbdaczhAB1corq3h26hq6tEriip5tTmkbxpwqESkSkQO1TEUiEhZ/0X96fjaN42xoeFN3/A2onwNlwAc4LaES6qZblX8CmaraE5gKvF3bSqczREC18blb2LTnEI8M60yEDeVu6lktFxpVT0mq2tjr+upCk4Ro7hqUzYxVBeRuLPS6HBMG/L2K76CqPuqGxFmq+itVPdHZ0BN2/6+qe1S11H06FmcwxDpXUl7JC9PX0rddUwZ3Cenbt4wJarcOyCI1KZY/2dDwpg74exXfVBFJ9nneVESmnOBlC4GOIpIlIjHAKJwhAXy36zsk9Qicoazr3NtzNrLzQKkN5W5MgMXHRHLf4A4s2FjIzDWndjjemGr+HuJLca/cA0BV93KCniRUtQK4F5iCEzzjVXW5iDwpIiPc1e4TkeUisgRnOPlbTvYDnMiBknJembWe8zulck775nW9eWNMDdedlUF6s3ie/nw1VTaooTkN/gZUlYhkVD8RkUxq6d28JlWd7N7Um62q/8+d97iqTnIfP6aq3VW1l6peqKqrTv4jHN/Yr/LYd6icX9hQ7sbUi5ioCB4a2okV2w/w2Xd1PmaiaUD8Daj/Br4Wkb+LyD+AWcBjgSurbuwuLmXs1xu4rEdrzmjb5MQvMMbUiRG92tK5ZRLPTl1DeWWV1+WYEOXvRRKf4/RevhoYhzMS6OEA1lUnXv5yHaUVVTx0sQ3lbkx9iowQHh7WmQ27DzJhkQ0Nb06NvxdJ/ARnHKgxwMPA34EnAlfW6cvfe4h3521m5JlpZKcmel2OMQ3OkK4tODMjmeenraWk3IaGNyfP30N89wNnAZtU9UKgD7Dv+C/x1vPTnI4r77eh3I3xRPXQ8DsOlPD3uZu8LseEIH8DqkRVSwBEJNa9mCForzpYV1DExMX53HhOO9okx3tdjjENVv/s5pzXMYW/zlxHUUm51+WYEONvQOW790F9AkwVkU+BoP1K9Ocv1hAfHck9F2Z7XYoxDd4vhnVh76Fy/jbbhoY3J8ffiySuUtV9qvoE8BvgDeCEw214YWn+Pv69bAe3n9ee5jaUuzGe65HWhEt7tOKN2XnsKS498QuMcZ10l96qOktVJ7k9lAedp6espmlCNHecl+V1KcYY10NDO3O4vJKXv7Sh4Y3/wmrMibnr9zB77W7uHtSBJBvK3Zig0aFFIiP7pvGPeZvYui/o71AxQSJsAkpV+dOUVbRqHMdN/dt5XY4xpob7hzj3Iz4/zQY1NP4Jm4CavrKAbzbv4/4hHYmLtqHcjQk2bZPjufGcdkxYlM+6gmKvyzEhIGwC6pzs5vz6sq6M7JvmdSnGmGO458Js4qMjeXaqDWpoTixsAioxNoqfnNfehnI3Jog1T4zl9vPaM/m7HXyXv9/rckyQs7/mxph6dcd5WSQnRPO0DQ1vTsACyhhTr5Liorl7UDZfrdnFvLw9XpdjgpgFlDGm3v1X/0xaNY7jT5+vsqHhzTFZQBlj6l1cdCT3XdSRxZv3MX1lgdflmCBlAWVMkBORN0WkQESWHWP5IBHZLyLfutPj9V3jqbgmJ43M5gk884UNDW9qZwFlTPB7Cxh+gnVmq2pvd3qyHmo6bdGRETx0cWdW7Shi0pJtXpdjgpAFlDFBTlW/Agq9riMQLu/Rmm6tG/Ps1DWUVdjQ8OZoFlDGhIf+IrJERP4tIt2PtZKI3CkiuSKSu2vXrvqsr1YREcIjwzqzufAQH+Ru8bocE2QsoIwJfYuBdqraC3gRZ9y2Wqnq66qao6o5qamp9Vbg8QzqnMpZmU15cfpaDpfZ0PDmCAsoY0Kcqh5Q1WL38WQgWkRSPC7LbyLCL4Z3oaColLfmbPS6HBNELKCMCXEi0kpExH3cD2e/Dqk7YM/KbMaFnVN5ddZ69h+2oeGNwwLKmCAnIuOAuUBnEckXkdtF5C4RuctdZSSwTESWAC8AozQE7359eFhn9h8u5/WvbFBD44jyugBjzPGp6ugTLH8JeKmeygmY7m2acEWvNrz59UZuHpBJi6Q4r0syHrMWlDEmaDw0tBNllVW8PGOd16WYIGABZYwJGlkpjbg2J533FmxmS+Ehr8sxHrOAMsYElfsv6kiECM/Z0PANngWUMSaotGoSx80DMvn4m62s2VnkdTnGQwENKBEZLiKrRWSdiDx6nPV+LCIqIjmBrMcYExp+dkE2iTFRPDPFBjVsyAIWUCISCbwMXAJ0A0aLSLda1ksC7gfmB6oWY0xoadoohjvOb88XK3byzea9XpdjPBLIFlQ/YJ2q5qlqGfA+cGUt6/0eeAooCWAtxpgQc9u5WTRvFMPT1opqsAIZUG0B394f89153xORM4F0Vf3seBsKtg4ujTGBlxgbxT0XdmDO+j18vXa31+UYD3h2kYSIRADPAmNOtG4wdnBpjAm8G87JoG1yPE9PsaHhG6JABtRWIN3neZo7r1oScAYwU0Q2AucAk+xCCWNMtdioSO4f0pEl+fuZsnyH1+WYehbIgFoIdBSRLBGJAUYBk6oXqup+VU1R1UxVzQTmASNUNTeANRljQszVfdqSndqIJyat4PNlO6wl1YAELKBUtQK4F5gCrATGq+pyEXlSREYE6n2NMeElKjKCZ6/tTWJcFHf9YxHXvT6Ppfn7vC7L1AMJtW8jOTk5mptrjSzjPRFZpKohe0g61Palisoq3l+4heemrmHPwTJ+1LsNjwzvQtvkeK9LM6fpWPuS9SRhjAkJUZER3HhOO2Y+Moi7B2Xz72U7uPCZmTz1+SqKSmwMqXBkAWWMCSlJcdH8YngXZjw8iMt6tOaVmesZ9PRM/j5vExWVVV6XZ+qQBZQxJiS1TY7nuet6M+negWS3SOQ3nyxj2F++YvrKnXYhRZiwgDLGhLSeacl8cOc5vH5TX1Th9rdzuWHsfJZt3e91aeY0WUAZY0KeiHBx91ZMefB8nriiGyu3H+CKl75mzPgl7NhvvaiFKgsoY0zYiI6M4JaBWcx85ELuPK89/1yyjUHPfMmzX6zmYGmF1+WZk2QBZYwJO03io3ns0q5MH3MBQ7u14oUZ67jg6ZmMW7CZyio7PxUqLKCMMWErvVkCL47uw8d3D6Bd8wQe++g7Ln1+NrPWWKfTocACyhgT9vpkNGXCXf356w1ncri8kpvfXMBNb8xn1Y4DXpdmjsMCyhjTIIgIl/ZozdSHzufXl3Vlaf5+Ln1+No9OXErBAbuQIhhZQBljGpTYqEh+cl57Zj0yiFsHZjFxcT6DnpnJ89PWcqjMLqQIJhZQxpgGKTkhht9c3o2pD17ABZ1SeW7aGi58ZiYf5m6xCymChAWUMaZBy0xpxCs39mXCXf1p3SSeRyYs5YoXv+Y/62wUX69ZQBljDJCT2YyP7x7AC6P7sP9wOTeMnc9tby1kXUGR16U1WBZQxhjjEhFG9GrD9DEX8NglXVi4sZBhf5nNf3/8HbuLS70ur8GxgDLGmBrioiP56QXZzHrkQm48O4MPFm5h0NMzefnLdZSUV3pdXoNhAWWMMcfQrFEMv7vyDKY8eD79s5vz9JTVDH5mJh9/k0+VXUgRcBZQxhhzAtmpifztv3IYd8c5NE+M5cEPlnDly/9h9tpdNrRHAFlAGWOMn/pnN+fTewby3HW92FNcyk1vLOCCp2fywvS1bN132Ovywo4FlDFBTkTeFJECEVl2jOUiIi+IyDoRWSoiZ9Z3jQ1JRIRwVZ80Zjw8iOeu60Va03ienbqGc5+awY1j5/Ppt1vtPFUdifK6AGPMCb0FvAS8c4zllwAd3els4BX3pwmguOhIruqTxlV90thSeIgJi/KZsCif+9//lqS4KEb0asM1Oen0SmuCiHhdbkiygDImyKnqVyKSeZxVrgTeUedkyDwRSRaR1qq6vV4KNKQ3S+DBoZ24/6KOzMvbw4eL8pm4OJ9352+mY4tErslxgiw1KdbrUkOKBZQxoa8tsMXneb477wcBJSJ3AncCZGRk1EtxDUlEhDCgQwoDOqTwuyu789nS7YzP3cIfJq/iqc9Xc2HnVEb2TWdwlxbERNkZlhOxgDKmAVHV14HXAXJycuzyswBqHBfN6H4ZjO6XwbqCIj5clM9Hi7cybWUBzRvF8KM+bbkmJ40urRp7XWrQsoAyJvRtBdJ9nqe580yQ6NAiiccu6cojF3fmq7W7+DA3n3fmbuSNrzfQo20TrslJY0SvNiQnxHhdalCxgDIm9E0C7hWR93Eujthv55+CU1RkBIO7tGRwl5YUHizj02+3Mj43n8c/Xc7//GslQ7u35NqcdM7tkEJkhF1YYQFlTJATkXHAICBFRPKB3wLRAKr6KjAZuBRYBxwCbvWmUnMymjWK4daBWdw6MItlW/czYVE+n3y7lc+Wbqd1kziuPrMtI/umk5XSyOtSPSOhdhd0Tk6O5ubmel2GMYjIIlXN8bqOU2X7UvAprahk+soCPszdwqw1u6hSOCuzKdf0TefSnq1JjA3PNsWx9qXw/LTGGBOCYqMiubRHay7t0ZqdB0qYuDifCbn5/GLiUp7453IuOaM11+ak0S+rWYO4t8oCynhDFYq2w65VULAK9m6AiCiIjofoBHeKh5hG7rx4iG7ks9xnWVQcNICd1TQsLRvHcfegDvzsgmwWb97Lh7n5/GvpdiYuzqdd8wRGnpnGj/um0SY53utSAyagASUiw4HngUhgrKr+scbyu4B7gEqgGLhTVVcEsiZTz6qDqGAl7FoNu9yfBaugdP+R9WIbO+uWHwStOsk3kSOhFZ0AMQlHB5nfgeezXvWyRikQZTdXGu+ICH3bNaNvu2Y8fkU3Pl+2gw9z8/nz1DU8O20N53ZIYWTfNIZ1b0VcdKTX5dapgJ2DEpFIYA0wFOfGwYXAaN8AEpHGqnrAfTwCuFtVhx9vu3bcPEipwoFtPgFUHUirjw6ihOaQ2hVSO0ML92dqV0hMPbKdyjIoPwTlh6Hs0JHH5Qfdn4ehrPrxIZ+fh2os851fY3v4+Xt//YfQ6eJaF9k5KOOlLYWHnB4rFuWzdd/h77tXuvrMNPqkJxMRQlcBenEOqh+wTlXz3ALex+mS5fuAqg4nVyP8/qthPKMKB7Y6LaBdq44E0q7VUOrz35mQ4gRQz2sgtYsztejqtEiOR8RpsUTFQnzTwH2GitKjg6v8UI0wdB+37BaYGow5TenNEnhoaCceuKgjc/P28GHuFiYscrpXSk2KZUjXFgzt1pIB2Skh27IKZEDV1v3KDzqwFJF7gIeAGGBwbRuy7lk8oAr7890QWuUTSKuhrOjIeo1SnfDpeR206HIkjE4URF4Sgeg4Z6KZ19UYc1oiIoSBHVIY2CGFJ0vKmb5yJ9NWFDDp222MW7CF+OhIzu+UwpCuLbmoa0uaNQqdm4E9v0hCVV8GXhaR64FfAzfXso51zxIoqrB/S43Dcu7PsuIj6zVq4RyO6z36yGG51C7QqLl3tRtjjtI4Lvr7HtZLKyqZl1fI1BU7mLaigCnLdxIh0LddU4Z2a8mQri1pn5rodcnHFciAOtnuV97HGSbA+KP6MFVFiXPOpqIEKqp/lkJl6dHzKn2WlRbBnnVOIO1e88MgatEFet/gc56oCyRYS8OYUBIbFckFnVK5oFMqv79SWbb1AFNX7mTqip38YfIq/jB5FdmpjRjarRVDu7Wgd3rToOu9IpABtRDoKCJZOME0CrjedwUR6aiqa92nlwFrCRflJc4f/z1rnXMblaVuoJT6BEjNEKmxTm3hU+nz83QktnSCp/cNRx+asyAyJuyICD3SmtAjrQkPDe1E/t5DTFuxk2krCxg7O49XZ60nJTGGwV1aMLRbK87tkEJ8jPfnrQIWUKpaISL3AlNwLjN/U1WXi8iTQK6qVvcfNgQoB/ZSy+G9oFdVCYUboGDFkWnnCihcf/zLpSOinft3qi8IiIqFyOrHcRAVA3GNITLGfe7Oi4rzmVfztcfbns+86HiIDe6mvTEmcNKaJnDLwCxuGZjF/sPlzFqzi6krdvLv73YwPjefuOgIzu2QytBuLRjcpaVn41hZV0f+qr6fZ+eKo8No12qnRQOAQLMsaNHNnbpCSieITfphSETYWDChzi4zN+GmrKKKBRsKmeYeCty67zAi0Cc9+ftDgdmpiXXei8Wx9iULqNoc3uucn9m53PlZHUYlPvfzJLZyLkGuDqIW3ZxzNjENt2PHhsYCyoQzVWXl9iKmrtjJ1JU7WLbVuY0kK6WRewl7K/q2q5vzVtYXX23KD7uXUPuG0Uoo2nZkndgmTgB1vxpadj8SRnauxhgTxkSEbm0a061NY+4f0pHt+w8zbcVOpq4s4K05G/nb7A00TYhmcJeWDO3WgvM6ptKojjuzbRgBVVkBhXlQsPzoMCrM4/t7gyNjIbUTZJ3vhFB1GDVua/28GWMavNZN4rmpfyY39c+kqKScr9bsZtrKnUxbuZOJi/OJiYrg3A7O/VZDuragReO4037P8Aqo6ptLC1YeCaOCFbBrjXOFHIBEQLP2zuG5HtccCaOmWRAZXv8cxhgTCElx0VzWszWX9WxNeWUVuRv3fn8ocMaqAn71MfRKT+Zi936rTi1P7bxV+JyDWj8Dxt98dHc7SW3c80RdoUX3I32/RYdv77+m/tg5KGOOpqqs2VnM1BU7mLqygCVb9gGQ2TyBzx84/5hdLoX/OaimmU6LyPfChUD15WaMMeYHRITOrZLo3CqJewd3ZOeBEqavLGDD7uJT6g8wfAKqWXu4/FmvqzDGGONq2TiO688+9f5T7WYcY4wxQckCyhhjTFCygDLGGBOULKCMMcYEJQsoY4wxQckCyhhjTFCygDLGGBOULKCMMcYEpZDr6khEdgGbjrE4Bdhdj+X4IxhrguCsK9RqaqeqqfVZTF2yfalOWE3+OVFNte5LIRdQxyMiucHWN1ow1gTBWZfVFDyC8XNbTf4Jp5rsEJ8xxpigZAFljDEmKIVbQL3udQG1CMaaIDjrspqCRzB+bqvJP2FTU1idgzLGGBM+wq0FZYwxJkxYQBljjAlKYRNQIjJcRFaLyDoReTQI6nlTRApEZJnXtVQTkXQR+VJEVojIchG5PwhqihORBSKyxK3pd17XVE1EIkXkGxH5l9e11Cfbl07M9qWTc6r7UlgElIhEAi8DlwDdgNEi0s3bqngLGO5xDTVVAGNUtRtwDnBPEPw7lQKDVbUX0BsYLiLneFxTtfuBlV4XUZ9sX/Kb7Usn55T2pbAIKKAfsE5V81S1DHgfuNLLglT1K6DQyxpqUtXtqrrYfVyE8wvT1uOaVFWL3afR7uT5lTsikgZcBoz1upZ6ZvuSH2xf8t/p7EvhElBtgS0+z/Px+Jcl2IlIJtAHmO9tJd83/78FCoCpqup5TcBfgF8AVV4XUs9sXzpJti+d0CnvS+ESUOYkiEgiMBF4QFUPeF2Pqlaqam8gDegnImd4WY+IXA4UqOoiL+swwc/2peM73X0pXAJqK5Du8zzNnWdqEJFonB3qXVX9yOt6fKnqPuBLvD/fMBAYISIbcQ5xDRaRf3hbUr2xfclPti/55bT2pXAJqIVARxHJEpEYYBQwyeOago6ICPAGsFJVn/W6HgARSRWRZPdxPDAUWOVlTar6mKqmqWomzu/SDFW90cua6pHtS36wfck/p7svhUVAqWoFcC8wBedk5XhVXe5lTSIyDpgLdBaRfBG53ct6XAOBm3C+xXzrTpd6XFNr4EsRWYrzx3Gqqjaoy7qDie1LfrN9qR5YV0fGGGOCUli0oIwxxoQfCyhjjDFByQLKGGNMULKAMsYYE5QsoIwxxgQlCyhTKxEZ1NB68TYmEGxfOnUWUMYYY4KSBVSIE5Eb3TFgvhWR19zOIotF5Dl3TJjpIpLqrttbROaJyFIR+VhEmrrzO4jINHccmcUiku1uPlFEJojIKhF517173piwZPtS8LGACmEi0hW4DhjodhBZCdwANAJyVbU7MAv4rfuSd4BfqmpP4Duf+e8CL7vjyAwAtrvz+wAP4IwL1B7n7nljwo7tS8EpyusCzGm5COgLLHS/kMXjdLNfBXzgrvMP4CMRaQIkq+osd/7bwIcikgS0VdWPAVS1BMDd3gJVzXeffwtkAl8H/mMZU+9sXwpCFlChTYC3VfWxo2aK/KbGeqfan1Wpz+NK7PfFhC/bl4KQHeILbdOBkSLSAkBEmolIO5z/15HuOtcDX6vqfmCviJznzr8JmOWOBpovIj9ytxErIgn1+imM8Z7tS0HIUjyEqeoKEfk18IWIRADlwD3AQZzByn6Nc5jiOvclNwOvujtNHnCrO/8m4DURedLdxjX1+DGM8ZztS8HJejMPQyJSrKqJXtdhTKizfclbdojPGGNMULIWlDHGmKBkLShjjDFByQLKGGNMULKAMsYYE5QsoIwxxgQlCyhjjDFB6f8DkAVtpPB//doAAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -862,26 +919,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test loss: 3.07\n", - "Test accuracy: 0.31\n", + "Test loss: 3.01\n", + "Test accuracy: 0.292\n", "Transfer learning with sgd, 143 Will save as: sgd_143.h5\n", "\n", "Train on 40000 samples, validate on 10000 samples\n", "Epoch 1/5\n", - "40000/40000 [==============================] - 520s 13ms/sample - loss: 2.2287 - accuracy: 0.4408 - val_loss: 3.5186 - val_accuracy: 0.2423\n", + "40000/40000 [==============================] - 314s 8ms/sample - loss: 4.2997 - accuracy: 0.0772 - val_loss: 4.1562 - val_accuracy: 0.1163\n", "Epoch 2/5\n", - "40000/40000 [==============================] - 369s 9ms/sample - loss: 1.3899 - accuracy: 0.6155 - val_loss: 3.3581 - val_accuracy: 0.2721\n", + "40000/40000 [==============================] - 313s 8ms/sample - loss: 2.9598 - accuracy: 0.2925 - val_loss: 3.7121 - val_accuracy: 0.1834\n", "Epoch 3/5\n", - "40000/40000 [==============================] - 397s 10ms/sample - loss: 1.1638 - accuracy: 0.6701 - val_loss: 3.2724 - val_accuracy: 0.2805\n", + "40000/40000 [==============================] - 308s 8ms/sample - loss: 2.2507 - accuracy: 0.4176 - val_loss: 3.5173 - val_accuracy: 0.2055\n", "Epoch 4/5\n", - "40000/40000 [==============================] - 406s 10ms/sample - loss: 1.0167 - accuracy: 0.7068 - val_loss: 3.1367 - val_accuracy: 0.2920\n", + "40000/40000 [==============================] - 314s 8ms/sample - loss: 1.9260 - accuracy: 0.4882 - val_loss: 3.3816 - val_accuracy: 0.2294\n", "Epoch 5/5\n", - "40000/40000 [==============================] - 387s 10ms/sample - loss: 0.9011 - accuracy: 0.7366 - val_loss: 3.2977 - val_accuracy: 0.2867\n" + "40000/40000 [==============================] - 320s 8ms/sample - loss: 1.7384 - accuracy: 0.5294 - val_loss: 3.3455 - val_accuracy: 0.2383\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -895,26 +952,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test loss: 3.29\n", - "Test accuracy: 0.293\n", - "Transfer learning with , 134 Will save as: _134.h5\n", + "Test loss: 3.34\n", + "Test accuracy: 0.234\n", + "Transfer learning with , 134 Will save as: _134.h5\n", "\n", "Train on 40000 samples, validate on 10000 samples\n", "Epoch 1/5\n", - "40000/40000 [==============================] - 399s 10ms/sample - loss: 2.2892 - accuracy: 0.4299 - val_loss: 2.9765 - val_accuracy: 0.3043\n", + "40000/40000 [==============================] - 347s 9ms/sample - loss: 3.5644 - accuracy: 0.2048 - val_loss: 3.6720 - val_accuracy: 0.1923\n", "Epoch 2/5\n", - "40000/40000 [==============================] - 393s 10ms/sample - loss: 1.3324 - accuracy: 0.6267 - val_loss: 2.9896 - val_accuracy: 0.3093\n", + "40000/40000 [==============================] - 340s 8ms/sample - loss: 1.9743 - accuracy: 0.4882 - val_loss: 3.2574 - val_accuracy: 0.2538\n", "Epoch 3/5\n", - "40000/40000 [==============================] - 377s 9ms/sample - loss: 1.0588 - accuracy: 0.6937 - val_loss: 3.0433 - val_accuracy: 0.3094\n", + "40000/40000 [==============================] - 328s 8ms/sample - loss: 1.5411 - accuracy: 0.5856 - val_loss: 3.1358 - val_accuracy: 0.2729\n", "Epoch 4/5\n", - "40000/40000 [==============================] - 367s 9ms/sample - loss: 0.8770 - accuracy: 0.7419 - val_loss: 3.1616 - val_accuracy: 0.2932\n", + "40000/40000 [==============================] - 336s 8ms/sample - loss: 1.3056 - accuracy: 0.6422 - val_loss: 2.9784 - val_accuracy: 0.3023\n", "Epoch 5/5\n", - "40000/40000 [==============================] - 370s 9ms/sample - loss: 0.7246 - accuracy: 0.7834 - val_loss: 3.7246 - val_accuracy: 0.2812\n" + "40000/40000 [==============================] - 308s 8ms/sample - loss: 1.1171 - accuracy: 0.6865 - val_loss: 3.0662 - val_accuracy: 0.2998\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -928,26 +985,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test loss: 3.71\n", - "Test accuracy: 0.276\n", - "Transfer learning with , 143 Will save as: _143.h5\n", + "Test loss: 3.07\n", + "Test accuracy: 0.293\n", + "Transfer learning with , 143 Will save as: _143.h5\n", "\n", "Train on 40000 samples, validate on 10000 samples\n", "Epoch 1/5\n", - "40000/40000 [==============================] - 420s 11ms/sample - loss: 1.9014 - accuracy: 0.5020 - val_loss: 3.6519 - val_accuracy: 0.2592\n", + "40000/40000 [==============================] - 292s 7ms/sample - loss: 2.5548 - accuracy: 0.3758 - val_loss: 3.5283 - val_accuracy: 0.2420\n", "Epoch 2/5\n", - "40000/40000 [==============================] - 386s 10ms/sample - loss: 1.2588 - accuracy: 0.6435 - val_loss: 3.4258 - val_accuracy: 0.2693\n", + "40000/40000 [==============================] - 298s 7ms/sample - loss: 1.6084 - accuracy: 0.5657 - val_loss: 3.5689 - val_accuracy: 0.2573\n", "Epoch 3/5\n", - "40000/40000 [==============================] - 389s 10ms/sample - loss: 1.0651 - accuracy: 0.6920 - val_loss: 3.2162 - val_accuracy: 0.2883\n", + "40000/40000 [==============================] - 304s 8ms/sample - loss: 1.3539 - accuracy: 0.6249 - val_loss: 3.3887 - val_accuracy: 0.2747\n", "Epoch 4/5\n", - "40000/40000 [==============================] - 369s 9ms/sample - loss: 0.9419 - accuracy: 0.7228 - val_loss: 3.4428 - val_accuracy: 0.2700\n", + "40000/40000 [==============================] - 302s 8ms/sample - loss: 1.1834 - accuracy: 0.6632 - val_loss: 3.3174 - val_accuracy: 0.2810\n", "Epoch 5/5\n", - "40000/40000 [==============================] - 405s 10ms/sample - loss: 0.8316 - accuracy: 0.7556 - val_loss: 3.3397 - val_accuracy: 0.2860\n" + "40000/40000 [==============================] - 298s 7ms/sample - loss: 1.0600 - accuracy: 0.6968 - val_loss: 3.4617 - val_accuracy: 0.2727\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -961,8 +1018,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test loss: 3.37\n", - "Test accuracy: 0.282\n" + "Test loss: 3.47\n", + "Test accuracy: 0.27\n" ] } ], @@ -1002,7 +1059,7 @@ " print(f'Transfer learning with {optimizer}, {freeze_first_n_layers}', \"Will save as: \", saved_name)\n", " print()\n", "\n", - " history, model = transfer_from_mobilenet(optimizer, freeze_first_n_layers)\n", + " history, model = transfer_from_mobilenet(optimizer, freeze_first_n_layers, 128, 5)\n", " model.save(os.path.join(save_directory, saved_name))\n", " plot_training_history(history, model)\n", " \n", @@ -1014,7 +1071,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1023,20 +1080,20 @@ "text": [ "Train on 40000 samples, validate on 10000 samples\n", "Epoch 1/5\n", - "40000/40000 [==============================] - 375s 9ms/sample - loss: 2.0464 - accuracy: 0.4861 - val_loss: 3.9365 - val_accuracy: 0.2672\n", + "40000/40000 [==============================] - 284s 7ms/sample - loss: 2.4848 - accuracy: 0.3901 - val_loss: 3.5059 - val_accuracy: 0.2351\n", "Epoch 2/5\n", - "40000/40000 [==============================] - 365s 9ms/sample - loss: 1.3946 - accuracy: 0.6131 - val_loss: 4.0802 - val_accuracy: 0.2573\n", + "40000/40000 [==============================] - 279s 7ms/sample - loss: 1.5971 - accuracy: 0.5688 - val_loss: 3.3056 - val_accuracy: 0.2609\n", "Epoch 3/5\n", - "40000/40000 [==============================] - 285s 7ms/sample - loss: 1.1934 - accuracy: 0.6589 - val_loss: 4.2143 - val_accuracy: 0.2537\n", + "40000/40000 [==============================] - 296s 7ms/sample - loss: 1.3392 - accuracy: 0.6256 - val_loss: 3.4534 - val_accuracy: 0.2635\n", "Epoch 4/5\n", - "40000/40000 [==============================] - 269s 7ms/sample - loss: 1.0459 - accuracy: 0.6944 - val_loss: 4.3446 - val_accuracy: 0.2498\n", + "40000/40000 [==============================] - 279s 7ms/sample - loss: 1.1499 - accuracy: 0.6709 - val_loss: 4.0231 - val_accuracy: 0.2381\n", "Epoch 5/5\n", - "40000/40000 [==============================] - 271s 7ms/sample - loss: 0.9341 - accuracy: 0.7229 - val_loss: 4.1210 - val_accuracy: 0.2580\n" + "40000/40000 [==============================] - 285s 7ms/sample - loss: 1.0005 - accuracy: 0.7093 - val_loss: 3.8136 - val_accuracy: 0.2594\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1050,13 +1107,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test loss: 4.09\n", - "Test accuracy: 0.257\n" + "Test loss: 3.84\n", + "Test accuracy: 0.255\n" ] } ], "source": [ - "history, model = transfer_from_mobilenet('adam', 151) # Leave the final 2 conv layers alone, freeze all the bottleneck blocks\n", + "history, model = transfer_from_mobilenet('adam', 151, 128, 5) # Leave the final 2 conv layers alone, freeze all the bottleneck blocks\n", "model.save(os.path.join(save_directory, saved_name))\n", "plot_training_history(history, model)\n", "\n", @@ -1071,12 +1128,12 @@ "source": [ "Hmm. That's not what we were hoping for was it? \n", "\n", - "Why do we think this transfer attempt wasn't very successful?" + "**Why do you think this transfer attempt wasn't very successful?**" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1085,24 +1142,20 @@ "text": [ "Train on 40000 samples, validate on 10000 samples\n", "Epoch 1/5\n", - "40000/40000 [==============================] - 774s 19ms/sample - loss: 2.1691 - accuracy: 0.4281 - val_loss: 8.4871 - val_accuracy: 0.0914\n", + "40000/40000 [==============================] - 790s 20ms/sample - loss: 2.4210 - accuracy: 0.3914 - val_loss: 7.8928 - val_accuracy: 0.0157\n", "Epoch 2/5\n", - "40000/40000 [==============================] - 764s 19ms/sample - loss: 1.5432 - accuracy: 0.5659 - val_loss: 7.1069 - val_accuracy: 0.0848\n", + "40000/40000 [==============================] - 781s 20ms/sample - loss: 1.5454 - accuracy: 0.5875 - val_loss: 7.0846 - val_accuracy: 0.0205\n", "Epoch 3/5\n", - "40000/40000 [==============================] - 764s 19ms/sample - loss: 1.3159 - accuracy: 0.6191 - val_loss: 4.1085 - val_accuracy: 0.2927\n", + "40000/40000 [==============================] - 807s 20ms/sample - loss: 1.2732 - accuracy: 0.6531 - val_loss: 6.6283 - val_accuracy: 0.0151\n", "Epoch 4/5\n", - "40000/40000 [==============================] - 760s 19ms/sample - loss: 1.1607 - accuracy: 0.6575 - val_loss: 3.7374 - val_accuracy: 0.2752\n", + "40000/40000 [==============================] - 790s 20ms/sample - loss: 1.1043 - accuracy: 0.6931 - val_loss: 7.0993 - val_accuracy: 0.0331\n", "Epoch 5/5\n", - "40000/40000 [==============================] - 764s 19ms/sample - loss: 1.0305 - accuracy: 0.6899 - val_loss: 4.4847 - val_accuracy: 0.2994\n", - "WARNING:tensorflow:From /Users/tylerbettilyon/.local/share/virtualenvs/deep-learning-intro-1Adgpw9A/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "If using Keras pass *_constraint arguments to layers.\n", - "INFO:tensorflow:Assets written to: saved_modelsadam_train_all/assets\n" + "40000/40000 [==============================] - 792s 20ms/sample - loss: 0.9759 - accuracy: 0.7267 - val_loss: 5.0560 - val_accuracy: 0.0637\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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y3UBpPBYV7VyLOm0UfPQrZ8Tf4n/CBX+EziO8ji48lBTByv9Ay77QvLvX0QSMJagIpqp8vaaIZ6bn89XqIjKSYrnn/E5cN7gNaYmxXodn6khEOgNv+2xqBzyoqn/2KKTAaNQULnsB+l7ndPu9eRU07QrdL3OW+mjczusIQ0vZflg5BRZNhNWfQFUFpOU6KyHHN/I6uoCwBBWBVJVPVxby9PRVzN+wm6yUeB648DSuHtDK7lOKAKq6EugNh1eu3gS852lQgdTmTPjxTPj2NaclNf03zqNlXydRdb8MUlt6HaU3Kitg7aew6J+w4gMo2wep2TDop04y/9dPYPr/OfMgRgD7tIogVVXK1KVbeWZGPks37yU7PZH/u6Q7V/bLISHWboqMUOcCq1V1vdeBBFRMHPS/yXns3ghL34Ml78BHv4SPHnDm/et+GXS9BJIzvY42uFRh83ynpbTkXSjZDvFpzs/fYwy0PsOZWR6ccrNfcBJ57gBv4w4ACbdJxPPy8nTu3LlehxFSKiqr+GDRFsbPyGfV9n20aZLET4d24NI+2TY8vJ6JyDxVzavH870MzFfVZ2rY57sSdb/16yMgh+3Id5aeXzwJdqwEiYZ25zgfyKdd5IwOjBRFq50W5KKJsHM1RMc5M3P0GAMdz4fYhGPfU1oMzw6G2CRn4t6Y8Lhn8Xj1xhJUGCurqOLd+QU8++lqNuzcT+dmKfx0aHsu6tnSFvnzSH0mKBGJAzYD3VR124nKRly9UYVtS51W1ZJ3YPd65wO84/lOsuo0IjxHAe4rdBLwoomwaS4gTpdnjyuh6yhIzDj5MVZ9DG9cDkN+DsN+GfSQA+F49ca6+MLQwfJK3vpmAy98voYtew7SIzuNF67rx/DTmhFliakhuQCn9XTC5BSRRJzRas27w7kPwqZ5brJ617k2E5sMnS+AHldA+2Gh3ZIo3ecOdngbVs8ArYRm3WH4I06yTcup3fE6ngc9x8LMJ6Dr6LAe1WcJKozsK63g9VnrefGLtezYV0r/Nhk8dnlPhnTMtPnwGqargTe9DsJzIpCT5zzO/w2s/wqWTHJmqFgyyen2O+1i6H6FM3FtdAh87FWWO8lo8URY8R8o3++MwDvjNqcLr1nXUzv+iN9B/scweRzc9HFo/Mx1EJ5RNzB79pfz96/W8fKXa9lzoJyzOmYybmgfBrZr4nVoxiMikgwMB37kdSwhJSoa2p7lPEb+CdZ86rSslv4bvn0dkrOg26VOyyRnwJHBBfVBFQrmOklpybuwfwckpEPPMU5SajU4cPEkNYaRf4BJN8Ls5+D0nwXmuPXMElQI27GvlJdmruW1r9ezr7SC805rxrhhHeidaxNrNnSqWgLYN5QTiY6FjsOdx0UHYNU0p0U1/1X4ZoLTYjmUrFr0Ct48gDtWOdeUFv8Tdq2F6HjnBuSeV0GH84LX/djtMmcwyfRHocuFYXkfmSWoELR1z0Fe+Hw1b36zgdKKKkb2aMGt53Sga8tUr0MzJjzFJjqDDLqOcka6rfyv8+E961n46i/QpIN7j9XlkNX51M9XvM1puS2eCJu/BcRZUXjIPU53Y32MNhRxVjIePxAm3wbXvx92k/EGNUGJyAjgKSAaeFFVH6uhzBjgYUCBhap6TTBjCmUbd+7nuc9WM2luAZWqXNI7m5+c054OTSPjrnBjQkJ8itOt1nMM7N8Jyyc7yeSzP8Bnv4dmPY7MXpHR2v/jlhbD8g+cpLTmU9AqaN7TuS7W/XJvbi5ObekMtvjgDqfl2O/6+o/hFARtmLl7h/t3OP3kBcAc4GpVXeZTpiMwERimqrtEpKmqbj/RcSNuuCyQv30fz36az78XbCZahCvzcvjx2e3JbRyGw2QbuPq+D8pfkVhvAq54Kyz9l5OsCr5xtuX0d5JLt0shpfmx76ksh/xPnBF4K/8LFQcgvZVzTannmMC0xk5VVRW8cjFsXQy3zobUFl5HdAwvhpkPAPJVdY0bwFvAaGCZT5mbgfGqugvgZMkp0izbvJfxn+YzZfEW4mOiuH5wG24Z0o7maTXcgGeMCa6U5jDox85j13rnfqQl78CH98GH9zv3I3W/3JnUtsi9rrT0PTiw07k/qfc1TlLKHRhaXWlRUTDqL85M8VPugateD634TiCYCSob2OjzugAYWK1MJwAR+RKnG/BhVf2w+oGq3REflGDr07cbdjF+Rj4fL99Oo/gYfnJ2e248s62tVGtMqMhoDWfe6TwKVzqj7pZMcrrKPrjDKROTAJ1HOkmp/bnO9Eyhqkl7GPoLmPagM/y+2yVeR+QXrwdJxAAdgXOAHOBzEemhqrt9C6nqBGACOF0V9R1kIKgqs9fu5Jnp+czM30F6Uix3De/E9YPbkJZkM4sbE7KyOsPQ++Gc+2DrIlgxxUlgp13sXM8KF4NudRLtlHucARtJjb2O6KSCmaA2Abk+r3Pcbb4KgNmqWg6sFZHvcBLWnCDGVe++Xl3EE9NWMmfdLjIbxXP/BV34/qDWtkCgMeFExBmO3qKX15HUTXQMjH4GJpzjTLh7ybNeR3RSwbxLbQ7QUUTaunOGjQUmVyvzL5zWEyKSidPltyaIMdW7xQV7uO6l2RTsOsCvR3Vj5v8O5Udnt7fkZIypf817wBm3w4I3nMEdIS5oCUpVK4BxwFRgOTBRVZeKyCMiMsotNhUoEpFlwAzgXlUtClZM9e1geSV3vP0tTRrF8d/bz+L609vYshfGGG8N+Tk06Qjv3+HMAxjCgjrPh6pOUdVOqtpeVR91tz2oqpPd56qqd6lqV1XtoapvBTOe+vb7D1ewurCEP13Zi/SkEL6AaoxpOGITnK6+PRudhSBDmC0WFCQzV+3gb1+u4/rBrTmrY5bX4RhjzBGtBkH/H8Ls52HjN15Hc1yWoIJgz/5y7p20kHZZydx3wWleh2OMMcc67yFnufjJP4OKUq+jqZElqCB4cPIStheX8uSY3iTG2TUnY0wIik+Bi/8MhSvgi8e9jqZGlqAC7P2Fm/n3gs3cNqwjvWzWcWNMKOs43JlV/YvHnRWKQ4wlqADauucgD/xrCb1y07l1aHuvwzHGmJP73u+c2dX/PQ6qKr2O5iiWoAJEVbl30kJKKyp5ckwvYqLtV2uMCQPJTeCCP8Dm+TDrOa+jOYp9igbIa7PW88WqHfxy5Gm0y7LlMYwxYaT75dDpAmfY+c61XkdzmCWoAFhduI/fTlnOkE5ZXDuoFuvHGGNMKDi0uGFUDLx/m7M8fQiwBHWKyiuruOvtBSTERvPHK3oiYTKNvTHGHCUtG85/BNZ+Dt++7nU0gCWoUzZ+Rj4LC/bw6CU9aJZq6zgZY8JY3xug9Rkw9Zewd4vX0ViCOhULNu7m6en5XNK7JRf2DL1VKk3kEpF0EZkkIitEZLmIDPY6JhMBoqLg4r9AZamzLIfX4XgdQLg6UFbJXW8voGlKPL8e3d3rcEzD8xTwoap2AXrhTMhszKnL7ADn3A8rPnAWN/SQJag6+t1/l7NmRwmPX9mLtERbcNDUHxFJA4YALwGoaln1RT6NOSWDxznrXv3nHti/07MwLEHVwWffFfLq1+u58Yy2nN4h0+twTMPTFigE/iYi34rIiyKSXL2QiNwiInNFZG5hYWH9R2nCV3QMjHoG9hfBR7/yLAxLULW0q6SMe/+5kI5NG/HzEZ29Dsc0TDFAX+A5Ve0DlAD3VS+kqhNUNU9V87KybEZ9U0sterqLG74Oq6d7EoIlqFpQVR749xJ2lpTx5FW9bfFB45UCoEBVZ7uvJ+EkLGMC6+z/hSYd4P3bPVnc0BJULUxeuJn/LNrCncM70T07zetwTAOlqluBjSJyqAl/LrDMw5BMpIpNcLr6dm+AGY/W++ktQflp8+4DPPCvJfRtlc6PhrTzOhxjfga8ISKLgN7Abz2Ox0Sq1oOdxQ1nPQcb59TrqS1B+aGqypkItrJKeWJMb5sI1nhOVRe415d6quolqrrL65hMBDv30OKG4+p1ccOgftKKyAgRWSki+SJyzEVcEblBRApFZIH7+GEw46mrv3+1ji/zi3jgwq60yTxmsJQxxkS2hFS46El3ccMn6u20QUtQIhINjAcuALoCV4tI1xqKvq2qvd3Hi8GKp65WbSvm9x+uYFiXplw9INfrcIwxxhudzoceY9zFDevnkmcwW1ADgHxVXaOqZcBbwOggni/gyiqquHPiApLionns8h42EawxpmEb8ZjTmppcP4sbBjNBZQMbfV4XuNuqu1xEFrnzitXYRPHqhsOnp69iyaa9/O6yHjRNsYlgjTEN3KHFDTfNg9nPB/10Xl/tfx9oo6o9gWnAKzUV8uKGw3nrdzF+Rj6X981hRHebCNYYYwB3ccMR9bK4YTAT1CbAt0WU4247TFWLVPXQkJAXgX5BjMdvJaUV3D1xAS3SEnloVE2XzYwxpoESgQufAImGD+4I6uKGwUxQc4COItJWROKAscBk3wIi4ts0GUWIzMj86JTlrN+5n8fH9CI1wSaCNcaYo6Rlw/Bfw5pPYcEbQTtN0BKUqlYA44CpOIlnoqouFZFHRGSUW+w2EVkqIguB24AbghWPv2as2M4/Zm/g5rPaMahdE6/DMcaY0NTvB+7ihr+A4q1BOUVQr0Gp6hRV7aSq7VX1UXfbg6o62X1+v6p2U9VeqjpUVVcEM56T2VlSxr2TFtGleQp3n9/Jy1CMMSa0HVrcsPxg0BY39HqQRMhQVX7x7mL2HCjjiTG9iY+xiWCNMeaEMjvA0Pth+ftBWdzQEpTr3fmb+HDpVu4+vzNdW6Z6HY4xxoSHwT+D5j1hyr1wILAzblmCAgp27eehyUsZ0KYxN59lE8EaY4zfomNg9DNQsgM+eiCgh27wCaqqSrl74kJUlcfH9CI6ymaLMMaYWmnRC864Db59HVbPCNhhG3yCemnmWmav3clDF3cjt3GS1+EYY0x4Ory44W1QVhKQQzboBLVyazF/nLqS4V2bcWVejtfhGGNM+IpNdEb17d4A0wOzuGGDTVClFZXc8fYCUhNj+N1lNhGsMcacsjZnQN5NMOtZKJh7yodrsAnqzx+vYvmWvfzusp5kNor3OhxjjIkM5z0MqS3h3+OgouyUDtUgE9ScdTt5/rPVXJWXy/CuzbwOxxhjIsfhxQ2Xw8xTW9ywwSWofaUV3DVxATkZifzqYpsI1hhjAq7T96DHlfD5n2B73adY9StBici7InKhiIR9Qvu/95exadcBnhzTm0bxMV6HY4wxkWnEYxCf4nT11XFxQ38TzrPANcAqEXlMRDrX6Wwem7ZsG2/P3ciPzm5PXpvGXodjTJ2JyDoRWSwiC0Tk1K9GGxNoyZnu4oZzYfYLdTqEXwlKVT9W1e8DfYF1wMci8pWI/EBEwmI9ih37SrnvnUWc1iKVO8+ziWBNRBiqqr1VNc/rQIypUY8roOP3YP2XdVo3yu8+LhFpAlwLXAd8C7wBnAlcD5xT6zPXI1XlvncWU3ywgn/c3Ju4mLDvqTTGmNAnAle8DHHJzvNa8vca1HvAF0AScLGqjlLVt1X1Z0CjWp+1nv1zbgEfL9/Gz0d0pnPzFK/DMSYQFPhIROaJyC01FRCRW0RkrojMLSwsrOfwjHHFN6pTcgL/W1B/UdUaJ1gK9e6FDUX7+fX7SxnUrjE3ntHW63CMCZQzVXWTiDQFponIClX93LeAqk4AJgDk5eUFb11uY4LE376uriKSfuiFiGSIyE+DFFPAVFYpd/9zAVEi/OnKXkTZRLAmQqjqJvff7cB7wABvIzIm8PxNUDer6u5DL1R1F3BzcEIKnAmfr2HOul08PKobORk2EayJDCKSLCIph54D5wNLvI3KmMDzt4svWkRE1RmGISLRQFzwwjp1yzbv5YlpK7mge3Mu65vtdTjGBFIz4D13/sgY4B+q+qG3IRkTeP62oD4E3haRc0XkXOBNd9sJicgIEVkpIvkict8Jyl0uIioiAbmedbC8kjvfXkBaYhyPXmoTwZrIoqprVLWX++imqoGZOtqYEONvC+p/gR8BP3FfTwNePNEb3FbWeGA4UADMEZHJqrqsWrkU4HZgdi3iPqEnpn3Hym3F/O2G/jRODumGnjHGmOPwK0GpahXwnPvw1wAgX1XXAIjIW8BoYFm1cv8H/KFGDFUAABmVSURBVB64txbHPq5Za4r46xdruGZgK4Z2aRqIQxpjjPGAv/dBdRSRSSKyTETWHHqc5G3ZwEaf1wXuNt/j9gVyVfU/Jzm/X/dz7D1Yzt0TF9K6cRK/HHnaScIzxhgTyvy9BvU3nNZTBTAUeBV4/VRO7E48+wRw98nKquoEVc1T1bysrKzjlvv15GVs2XOAJ67qTbJNBGuMMWHN3wSVqKqfAKKq61X1YeDCk7xnE5Dr8zrH3XZICtAd+FRE1gGDgMl1HSixoWg/7y/azK1DO9C3VUZdDmGMJ0TkdhFJFcdLIjJfRM73Oi5jvOZvM6PUbfGsEpFxOInmZFMczQE6ikhbt/xYnBnRAVDVPUDmodci8ilwj6rWaWbmVk2SmHLbWbRuYvc7mbBzo6o+JSLfAzJw5rt8DfjI27CM8Za/Lajbcebhuw3ohzNp7PUneoOqVgDjgKnAcmCiqi4VkUdEZFTdQz6+Dk0bERttE8GasHPoPoiRwGuqutRnmzEN1klbUO5w8atU9R5gH/ADfw+uqlOAKdW2PXicsuf4e1xjIsw8EfkIaAvc7956UeVxTMZ47qQJSlUrReTM+gjGmAbqJqA3sEZV94tIY2rxRdCYSOXvNahvRWQy8E+g5NBGVX03KFEZ07AMBhaoaomIXIuzMOhTHsdkjOf8vWCTABQBw4CL3cdFwQrKmAbmOWC/iPTCue1iNc6tHMY0aP7OJGHdDcYET4WqqoiMBp5R1ZdE5CavgzLGa34lKBH5G84KnkdR1RsDHpExDU+xiNyPM7z8LPeWjliPYzLGc/5eg/rA53kCcCmwOfDhGNMgXYVzj+CNqrpVRFoBf/Q4JmM8528X3zu+r0XkTWBmUCIypoFxk9IbQH8RuQj4RlXtGpRp8Op6V2tHwKYKNyYARGQM8A1wJTAGmC0iV3gblTHe8/caVDFHX4PairNGlDHm1P0S6K+q2wFEJAv4GJjkaVTGeMzfLr6UYAdiTAMWdSg5uYqoe++GMRHD3/WgLhWRNJ/X6SJySfDCMqZB+VBEporIDSJyA/Afqk0RZkxD5O+3tIfc2ccBUNXdwEPBCcmYhkVV7wUmAD3dxwRVPWkXuohEi8i3IvLBycoaE478HWZeUyKzFQGNCRB3pOw7Jy14tNtxVgpIDXxExnjP3xbUXBF5QkTau48ngHnBDMyYSCcixSKyt4ZHsYjsPcl7c3AWDX2xfqI1pv752wr6GfAr4G2c0XzTgFuDFZQxDcEpDj76M/BznJWpjYlI/o7iKwHuC3Isxhg/uDfzblfVeSJyzgnK3QLcAtCqVat6is6YwPF3FN80EUn3eZ0hIlODF5Yx5gTOAEaJyDrgLWCYiLxevZCqTlDVPFXNy8rKqu8YjTll/l6DynRH7gGgqruwmSSM8YSq3q+qOaraBhgLTFfVaz0Oy5iA8zdBVbkTWAIgIm2oYXZzY4wxJlD8TVC/BGaKyGtuV8JnwP0ne5OIjBCRlSKSLyLHXMMSkR+LyGIRWSAiM0Wka+3CN6ZhU9VPVdUWDzURya8EpaofAnnASuBNnFU/D5zoPSISDYwHLgC6AlfXkID+oao9VLU38AfgidqFb4wxJlL5O1nsD3FuCswBFgCDgK9xloA/ngFAvqqucY/xFjAaWHaogKr63uuRjHUbGmOMcfnbxXc70B9Yr6pDgT7A7hO/hWxgo8/rAnfbUUTkVhFZjdOCuq2mA4nILSIyV0TmFhYW+hmyMcaYcOZvgjqoqgcBRCReVVcAnQMRgKqOV9X2OMt3PHCcMjZc1hhjGhh/Z5IocO+D+hcwTUR2AetP8p5NQK7P6xx32/G8BTznZzzGGGMinL8zSVzqPn1YRGYAacCHJ3nbHKCjiLTFSUxjgWt8C4hIR1Vd5b68EFiFMcYYQx1mJFfVz/wsVyEi44CpQDTwsqouFZFHgLmqOhkYJyLnAeXALuD62sZjjDEmMgV1yQxVnUK1hddU9UGf57cH8/zGGGPCly0rbYwxJiRZgjLGGBOSLEEZY4wJSZagjDHGhCRLUMYYY0KSJShjjDEhyRKUMcaYkGQJyhhjTEiyBGWMMSYkWYIyxhgTkixBGWOMCUmWoIwxxoQkS1DGhBkRSRCRb0RkoYgsFZFfex2TMcEQ1NnMjTFBUQoMU9V9IhILzBSR/6rqLK8DMyaQLEEZE2ZUVYF97stY96HeRWRMcFgXnzFhSESiRWQBsB2Ypqqzayhzi4jMFZG5hYWF9R+kMafIEpQxYUhVK1W1N5ADDBCR7jWUmaCqeaqal5WVVf9BGnOKLEEZE8ZUdTcwAxjhdSzGBJolKGPCjIhkiUi6+zwRGA6s8DYqYwIvqAlKREaIyEoRyReR+2rYf5eILBORRSLyiYi0DmY8xkSIFsAMEVkEzMG5BvWBxzEZE3BBG8UnItHAeJxvdwXAHBGZrKrLfIp9C+Sp6n4R+QnwB+CqYMVkTCRQ1UVAH6/jMCbYgtmCGgDkq+oaVS0D3gJG+xZQ1Rmqut99OQvngq8xxhgT1ASVDWz0eV3gbjuem4D/1rTDhssaY0zDExKDJETkWiAP+GNN+224rDHGNDzBnEliE5Dr8zrH3XYUETkP+CVwtqqWBjEeY4wxYSSYLag5QEcRaSsiccBYYLJvARHpA7wAjFLV7UGMxRhjTJgJWoJS1QpgHDAVWA5MVNWlIvKIiIxyi/0RaAT8U0QWiMjk4xzOGGNMAxPUyWJVdQowpdq2B32enxfM8xtjjAlfITFIwhhjjKnOEpQxxpiQZAnKGGNMSLIEZUwDtq+0gnH/mM/qwn0nL2xMPbMEZUwD9t22Yj7/rpCRT33BXz9fQ2WVLcxrQoclKGMasL6tMvj4rrM5q2MWj05ZzhXPf0X+dmtNmdBgCcqYBq5pagJ//Z9+PDW2N2t3lDDyL1/w3Kerqais8jo008BZgjLGICKM7p3NR3cOYWjnLH7/4Qouf+4rvttW7HVopgGzBGWMOaxpSgLPX9uPp6/uw4ad+7noLzMZPyPfWlPGE5agjDFHEREu7tWSaXedzfCuzfjj1JVc+uxXrNi61+vQTANjCcoYU6PMRvGM/35fxl/Tl827D3Dx0zP5yyerKLfWlKknlqCMMSd0Yc8WfHTnEEZ0b8ET077jkvFfsmyztaZM8FmCMsacVJNG8Tx9dR+ev7Yf2/aWMuqZmTw57TvKKqw1ZYLHEpQxYUZEckVkhogsE5GlInJ7fZ17RPfmTLtzCBf1bMFTn6xi1DMzWbJpT32d3jQwlqCMCT8VwN2q2hUYBNwqIl3r6+QZyXH8eWwf/vo/eRSVlDF6/Jc8/tFKSisq6ysE00BYgjImzKjqFlWd7z4vxlkQNLu+4xjetRnT7hzC6N4teXp6PqOe/pJFBbvrOwwTwSxBGRPGRKQN0AeYXcO+W0RkrojMLSwsDMr505PieGJMb16+IY/dB8q49Nmv+MOHKzhYbq0pc+osQRkTpkSkEfAOcIeqHjOsTlUnqGqequZlZWUFNZZhXZrx0Z1nc3nfbJ79dDUXPT2TbzfsCuo5TeSzBGVMGBKRWJzk9Iaqvut1PABpibH84Ype/P0H/SkpreDy577id1OWW2vK1FlQE5SIjBCRlSKSLyL31bB/iIjMF5EKEbkimLEYEylERICXgOWq+oTX8VR3TuemTL1zCFf1z+WFz9cw8i9fMG+9taZM7QUtQYlINDAeuADoClxdw0ijDcANwD+CFYcxEegM4DpgmIgscB8jvQ7KV2pCLL+7rCev3TSA0vIqrnj+K37zwTIOlFlryvgvJojHHgDkq+oaABF5CxgNLDtUQFXXufvsbj9j/KSqMwHxOg5/nNUxi6l3DuF3U5bz4sy1fLJiO3+4oif92zT2OjQTBoLZxZcNbPR5XUAdh8LWx2gkY0xwNIqP4dFLe/CPHw6kvLKKMS98za/fX8r+sgqvQzMhLiwGSdTnaCRjTHCc3iGTqXcM4bpBrfnbl+u44KkvmLWmyOuwTAgLZoLaBOT6vM5xtxljGqjk+BgeGd2dN28ehCqMnTCLh/69hJJSa02ZYwUzQc0BOopIWxGJA8YCk4N4PmNMmBjcvgkf3nEWN5zehldnrWfEU5/zVf4Or8MyISZoCUpVK4BxwFScqVgmqupSEXlEREYBiEh/ESkArgReEJGlwYrHGBNakuJieHhUN96+ZTDRIlzz4mwe+Ndi9llryriCOYoPVZ0CTKm27UGf53Nwuv6MMQ3UgLaN+e/tQ3j8o5W89OVaZqwo5PeX9+TMjpleh2Y8FhaDJIwxkS0xLpoHLurKpB8PJj4mimtfms397y5iyaY9toJvAxbUFpQxxtRGv9aNmXL7WTw57Tv++sUa3vxmI/ExUXRtmUqvnHR6ZKfRKzeNdpmNiIoKi1vBzCmwBGWMCSkJsdHcP/I0rhvcmvkbdrNo424WFexh4tyN/P2rdYBzb1W3lqn0yk2nZ04avXLSyclIxJkFykQKS1DGnExlOWxbAhu/gdK9MOReryNqEHIyksjJSGJUr5YAVFYpqwv3sdBNWIsKdvP3L9dR5nYBZiTF0iMnnV45afTMcRJXs9QEL38Ec4osQRlTXUkRFHzjJKSN38CmeVBxwNmX2QnOugfsm3q9i44SOjVLoVOzFK7Mc26xLKuoYuXWYhYW7GZRgZO4nv10B5VVCkCz1Hh6ukmrR046PbPTyEiO8/LHMLVgCco0bFWVULjiSDLaOBt2rnb2RcVA857Q73rIHQC5AyHNBp2GkriYKHrkpNEjJw1oDcCBskqWbt5zuJW1qGAP05ZtO/yeVo2T6JmT5j7S6Z6dRqN4+ygMRfa/YhqWg3ugYO6RZLRpntNtB5CU6SSivtc5yahFb4hL8jZeU2uJcdHktWlMns+EtHsOlLN00x4Wuknr2w27+WDRFsBpDHfIakQP91pWz5w0TmuRSkJstFc/gnFZgjKRSxWKVjuJ6FCX3fblgAICzbpBjysgZ4CTmBq3s667CJWWGMvpHTI5vcORe6t27CtlccEeFhbsZnHBHj7/bgfvzndmY4uJEjo3TzncPdgzJ52OzRoRG2135tQnS1AmcpSVwKb5R18/OrDT2RefBrn9oeslTjLK7gcJqd7GazyV2SieoV2aMrRLUwBUlS17Dh7uFlxUsIf/LNrMm99sACA+JopuLVPpmZNO+6aNyM1IJLdxEtnpidbaChJLUCY8qcLuDVAwx2khbZwNW5eAugviZXaCziOPXDvK7ARR9u3XHJ+I0DI9kZbpiYzo3gJwkta6ov0+SWs3b8/ZyIFqy9g3TYknt3ESuRmJ5GQkkds4kdyMJHIbJ9E8LcFaXnVkCcqEh4pS2LLQTUZu62jfVmdfbJLTIjrzTich5fSHJFsQz5w6EaFtZjJtM5MZ3dtZzq6qStleXMrGXfvZuHM/BbsOsHHnfjbu2s/c9bt4f9GWw6MIwRl92Dw14XDSOpzAGieRk5FIs5QEu+n4OCxBmdBUvPXoZLRlAVSWOfvSW0PbIW7raAA07QbR9qds6kdUlNA8LYHmaQk1rgxcXlnF1j0HjyQvn0T2+apCtu0tPap8XHQU2RmJ5NTQ+srJSKRJclyDvQHZarXxRlUlFG+BXetqfpS4KydHx0PLPjDwR05XXc4ASGnmVdTGnFRsdJTT3de45hGgB8sr2bT7wFEtr4JdByjYuZ+pm7eys6TsqPJJcdFHklfGoZaXk7xyGyeRlhhbHz+WJyxBmeA5uPfYxLN7vfvvhiMtIgCJcu4xymgDnS+AzM7uUO+eEBPvRfTGBEVCbDTtsxrRPqtRjftLSiuOSV7O8wPMWbuT4mrLkaQmxBxubbVIS6RpajxZjeJpmppA05R4mqbEk5EUF5bdiJagTN1VVsDegmpJaP2R54dG0B2SkO4koGbdocuFzvNDj7RciI7cb4KBJiIvAxcB21W1u9fxmMBJjo+hc/MUOjdPOWafqrL3QMXR17/c56sLS/gqv+iYBAbOsPmslHiy3ISVleImr9R4mqYk+GyPD6kBHZag6qKqypn6pvyg//9GRUNcMsQmOhf1j/nX53moXE9RhQO7jm39HH698cioOXBmXkhv5SScFqOPTkAZrSExo95/hAj2d+AZ4FWP4zD1SERIS4olLSmN7tlpNZY5UFZJYXEp24sPsr24lO173X/dx6bdB1mwcTdFJWWoHvv+xslxh5OVb/I6lMwOPU+KC/7nVIh8EgZAVRXsLzpOgnAfFQdP8K8fSab8gPO8suzk8ZyKqFgnUcVVT2S+yaz6tkSfBHiC5Hc4CcY6N6VWlMGejce5FrQeSvccHVtSppNwsvtB98uPTkIpLUMnuUY4Vf1cRNp4HYcJPYlx0bRqkkSrJieeBaW8soqifWVOItvrJK+jEltxKau376BwXynllcdmsuS4aJqm+iSwlASf7sUjySw9KbbOgzwi59OkdC/8qUMt3yTOB3ZMwtH/HnqenHXsvrr+W1UB5fvdZOn+W7b/2G3lNWzzLXdg95GEW15yJMHWlkQ7iaq8BNRnQbjo+COtoNyBx7aC4o/tdjChSURuAW4BaNWqlcfRmFATGx11eDTiiVRVKbsPlB9JXnsPtcacRFa4t5Slm/cyY+92Ssoqj3l/XHQU557WlOeu7VfrGCMnQcU1gpF/ql3SiI6LjKltDnc5HnBmUzgq4fkkMt/EV+Y+j0+Bxm2PJKFGze2G1gihqhOACQB5eXk1dOYYc3JRUULj5DgaJ8fVeF3MV0lpxVHdioVuS6xlet2WPQlqghKREcBTQDTwoqo+Vm1/PE4fej+gCLhKVdfV6WTRMTDg5lOKN2xFRTnde3HJkJx58vLGGBMEyfExtI2PoW1mckCOF7SvyiISDYwHLgC6AleLSNdqxW4CdqlqB+BJ4PfBiscYY0x4CWZfzgAgX1XXqGoZ8BYwulqZ0cAr7vNJwLnSUG+ZNqYWRORN4Gugs4gUiMhNXsdkTKAFs4svG9jo87oAGHi8MqpaISJ7gCbADt9CdrHXmKOp6tVex2BMsIXF1XBVnaCqeaqal5WV5XU4xhhj6kEwE9QmINfndY67rcYyIhIDpOEMljDGGNPABTNBzQE6ikhbEYkDxgKTq5WZDFzvPr8CmK5a073NxhhjGpqgXYNyrymNA6biDDN/WVWXisgjwFxVnQy8BLwmIvnATpwkZowxxgT3PihVnQJMqbbtQZ/nB4ErgxmDMcaY8CTh1qMmIoXA+hMUyaTaKMAQYXHVTrjG1VpVQ24kj9WbgLO4aqdO9SbsEtTJiMhcVc3zOo7qLK7asbjqV6j+XBZX7URaXGExzNwYY0zDYwnKGGNMSIrEBDXB6wCOw+KqHYurfoXqz2Vx1U5ExRVx16CMMcZEhkhsQRljjIkAlqCMMcaEpIhJUCIyQkRWiki+iNzndTyHiMjLIrJdRJZ4HcshIpIrIjNEZJmILBWR272O6RARSRCRb0RkoRvbr72O6RARiRaRb0XkA69jCRSrN/4L1XoTynUGTq3eRESC8nNxRK/8HRjhdRDVVAB3q2pXYBBwawj9vkqBYaraC+gNjBCRQR7HdMjtwHKvgwgUqze1Fqr1JpTrDJxCvYmIBIV/iyN6QlU/x5lnMGSo6hZVne8+L8b548n2NiqHOva5L2Pdh+cjeUQkB7gQeNHrWALI6k0thGq9CdU6A6debyIlQdW0OKLnfzjhQETaAH2A2d5GcoTbJbAA2A5MU9VQiO3PwM+BKq8DCSCrN3UUavUmROsMnGK9iZQEZepARBoB7wB3qOper+M5RFUrVbU3zhpiA0Sku5fxiMhFwHZVnedlHCY0hGK9CbU6A4GpN5GSoPxZHNH4EJFYnEr2hqq+63U8NVHV3cAMvL8WcQYwSkTW4XSDDROR170NKSCs3tRSqNebEKozEIB6EykJyp/FEY1LRARnLa7lqvqE1/H4EpEsEUl3nycCw4EVXsakqverao6qtsH525quqtd6GVOAWL2phVCtN6FYZyAw9SYiEpSqVgCHFkdcDkxU1aXeRuUQkTeBr4HOIlIgIjd5HRPON5vrcL7RLHAfI70OytUCmCEii3A+QKepasQM6w4lVm9qLVTrTcTWGZvqyBhjTEiKiBaUMcaYyGMJyhhjTEiyBGWMMSYkWYIyxhgTkixBGWOMCUmWoMwJicg5kTR7tzH1wepNYFiCMsYYE5IsQUUIEbnWXRNmgYi84E4euU9EnnTXiPlERLLcsr1FZJaILBKR90Qkw93eQUQ+dteVmS8i7d3DNxKRSSKyQkTecO+oNybsWb0JbZagIoCInAZcBZzhThhZCXwfSAbmqmo34DPgIfctrwL/q6o9gcU+298AxrvrypwObHG39wHuwFkzqB3OHfXGhDWrN6EvxusATECcC/QD5rhf0hJxpt2vAt52y7wOvCsiaUC6qn7mbn8F+KeIpADZqvoegKoeBHCP942qFrivFwBtgJnB/7GMCSqrNyHOElRkEOAVVb3/qI0iv6pWrq7zWpX6PK/E/m5MZLB6E+Ksiy8yfAJcISJNAUSksYi0xvn/vcItcw0wU1X3ALtE5Cx3+3XAZ+4KoQUicol7jHgRSarXn8KY+mX1JsRZRo8AqrpMRB4APhKRKKAcuBUowVm87AGcrour3LdcDzzvVqQ1wA/c7dcBL4jII+4xrqzHH8OYemX1JvTZbOYRTET2qWojr+MwJpxYvQkd1sVnjDEmJFkLyhhjTEiyFpQxxpiQZAnKGGNMSLIEZYwxJiRZgjLGGBOSLEEZY4wJSf8PD6DaLjUFNEYAAAAASUVORK5CYII=\n", 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" ] @@ -1117,13 +1170,29 @@ "# lets try unfreezing a lot more. We have a reasonable amount of training data, so lets try!\n", "\n", "# WARNING, THIS TOOK ~2 hours on my MacbookPro\n", - "history, model = transfer_from_mobilenet('adam', 0) # unfreeze it all!\n", + "history, model = transfer_from_mobilenet('adam', 0, 128, 5) # unfreeze it all!\n", "plot_training_history(history, model)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ouch — we overfit extremely early and thereafter made very little progress on our validation scores. Brutal." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "model.save(os.path.join(save_directory, \"adam_train_all.h5\"))" + ] + }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1132,20 +1201,20 @@ "text": [ "Train on 40000 samples, validate on 10000 samples\n", "Epoch 1/5\n", - "40000/40000 [==============================] - 764s 19ms/sample - loss: 0.9159 - accuracy: 0.7221 - val_loss: 3.4448 - val_accuracy: 0.3675\n", + "40000/40000 [==============================] - 724s 18ms/sample - loss: 4.9200 - accuracy: 0.0125 - val_loss: 4.6830 - val_accuracy: 0.0119\n", "Epoch 2/5\n", - "40000/40000 [==============================] - 759s 19ms/sample - loss: 0.8169 - accuracy: 0.7519 - val_loss: 3.8265 - val_accuracy: 0.3655\n", + "40000/40000 [==============================] - 720s 18ms/sample - loss: 4.6629 - accuracy: 0.0195 - val_loss: 4.5614 - val_accuracy: 0.0241\n", "Epoch 3/5\n", - "40000/40000 [==============================] - 759s 19ms/sample - loss: 0.7318 - accuracy: 0.7747 - val_loss: 2.6605 - val_accuracy: 0.4732\n", + "40000/40000 [==============================] - 717s 18ms/sample - loss: 4.4994 - accuracy: 0.0335 - val_loss: 4.4482 - val_accuracy: 0.0486\n", "Epoch 4/5\n", - "40000/40000 [==============================] - 757s 19ms/sample - loss: 0.6492 - accuracy: 0.7961 - val_loss: 2.3039 - val_accuracy: 0.5131\n", + "40000/40000 [==============================] - 717s 18ms/sample - loss: 4.3510 - accuracy: 0.0586 - val_loss: 4.2846 - val_accuracy: 0.1031\n", "Epoch 5/5\n", - "40000/40000 [==============================] - 761s 19ms/sample - loss: 0.5848 - accuracy: 0.8159 - val_loss: 2.5467 - val_accuracy: 0.4981\n" + "40000/40000 [==============================] - 722s 18ms/sample - loss: 4.1490 - accuracy: 0.0995 - val_loss: 4.0376 - val_accuracy: 0.1710\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1154,61 +1223,542 @@ "needs_background": "light" }, "output_type": "display_data" - }, + } + ], + "source": [ + "# Lets slow down the learning rage to try and avoid overfitting so fast.\n", + "\n", + "history, model = transfer_from_mobilenet(SGD(learning_rate=0.001), 0, 128, 5) # unfreeze it all!\n", + "plot_training_history(history, model)\n", + "model.save(os.path.join(save_directory, \"SDG_train_all_slow.h5\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Test loss: 2.51\n", - "Test accuracy: 0.501\n" + "Train on 40000 samples, validate on 10000 samples\n", + "Epoch 1/10\n", + "40000/40000 [==============================] - 732s 18ms/sample - loss: 3.7127 - accuracy: 0.1872 - val_loss: 3.4814 - val_accuracy: 0.2724\n", + "Epoch 2/10\n", + "40000/40000 [==============================] - 721s 18ms/sample - loss: 3.4166 - accuracy: 0.2386 - val_loss: 3.0787 - val_accuracy: 0.3426\n", + "Epoch 3/10\n", + "40000/40000 [==============================] - 744s 19ms/sample - loss: 3.1241 - accuracy: 0.2939 - val_loss: 2.7560 - val_accuracy: 0.3978\n", + "Epoch 4/10\n", + "40000/40000 [==============================] - 753s 19ms/sample - loss: 2.8458 - accuracy: 0.3428 - val_loss: 2.4493 - val_accuracy: 0.4472\n", + "Epoch 5/10\n", + "40000/40000 [==============================] - 783s 20ms/sample - loss: 2.5980 - accuracy: 0.3812 - val_loss: 2.1879 - val_accuracy: 0.4903\n", + "Epoch 6/10\n", + "40000/40000 [==============================] - 711s 18ms/sample - loss: 2.3879 - accuracy: 0.4154 - val_loss: 1.9617 - val_accuracy: 0.5309\n", + "Epoch 7/10\n", + "40000/40000 [==============================] - 709s 18ms/sample - loss: 2.2147 - accuracy: 0.4469 - val_loss: 1.8084 - val_accuracy: 0.5555\n", + "Epoch 8/10\n", + "40000/40000 [==============================] - 712s 18ms/sample - loss: 2.0601 - accuracy: 0.4757 - val_loss: 1.6683 - val_accuracy: 0.5801\n", + "Epoch 9/10\n", + "40000/40000 [==============================] - 740s 19ms/sample - loss: 1.9338 - accuracy: 0.5009 - val_loss: 1.5760 - val_accuracy: 0.5930\n", + "Epoch 10/10\n", + "40000/40000 [==============================] - 744s 19ms/sample - loss: 1.8260 - accuracy: 0.5225 - val_loss: 1.4829 - val_accuracy: 0.6104\n" ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "# That's a little more like it! Lets keep going... \n", - "history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.2, verbose=True)\n", + "# Okay, that actually looks quite encouraging, if a bit slow... lets train it some more!\n", + "history = model.fit(x_train, y_train, batch_size=128, epochs=10, validation_split=0.2, verbose=True)\n", "plot_training_history(history, model)\n", + "model.save(os.path.join(save_directory, \"SDG_train_all_slow_further.h5\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summary of Results so Far:\n", "\n", - "loss, accuracy = model.evaluate(x_test, y_test, verbose=False)\n", - "print(f'Test loss: {loss:.3}')\n", - "print(f'Test accuracy: {accuracy:.3}')" + "Slow and steady wins the race this time around. Based on the curve, and lack of overfitting we could probably continue to train this final model for several more epochs and continue to see improvements. I'll leave that as an exercise for your GPU.\n", + "\n", + "Taken together, the above experiments suggest fine tuning isn't working out for these two data sets and MobileNetV2. We got the best results from freezing the whole thing, or retraining the whole thing (but only when we train slowly!!). With fine tuning and a totally frozen pretrained network we ended up overfitting by a lot. \n", + "\n", + "This could also be a good time to apply data augmentation to synthetically increase our dataset and make the task harder for the network to memorize. \n", + "\n", + "All in all, this is about what we expected. CIFAR and ImageNet really are not that similar, and with CIFAR we do have enough data to justify training the whole network. Starting with pretrained weights has been showen to generally improve the speed of reaching high performance, and this is something else you could easily test in this case with slight modifications to the above code.\n", + "\n", + "**We need to experiment with fine tuning at a much lower learning rate with plain SGD to fully validate our hypothesis.** A slow rate worked very will with the full network unfrozen, but it might also work well with fine tuning. On the other hand, the more agressive `adam` performed horribly in both fine tuning and training the whole network.\n", + "\n", + "\n", + "# Additional Things To Note:\n", + "\n", + "\n", + "When fine tuning, the \"classification head\" is working with an ever changing feature extractor, rather than the fixed target which happens when we use a pretrained network as a feature extractor. Similarly, there are many more parameters being changed and more gradients being propogated backwards. This can also slow convergance and also cause the divergence and overfitting we observed.\n", + "\n", + "The relationship between the layers is complex and fairly inscrutable. When we change the weights of the final layers without allowing the weights at the early layers to also update accordingly we are changing the relationships between the layers, which might cause degraded performance if a tight relationship between layers has been learned.\n", + "\n", + "Fine tuning can increase the network's capacity and propensity to overfit. We saw that in all of these examples. Using slower learning rates and less agressive optimizers can help. Note that in all our experiemnts (even the feature extraction one) we quickly began overfitting, and after that our optimizations only improved results on the training set...\n", + "\n", + "Fine tuning works best when the underlying datasets are more similar. CIFAR100 and ImageNet are only kind of similar, the difference in image resolution could be playing a large role here. It's possible that starting with MobileNetv2 from scratch and NOT transfer learning would result in better outcomes, since the CIFAR100 dataset is relatively large.\n", + "\n", + "The dense layers in our classifier ALSO contribute to this model's ability to overfit. Sometimes a classifier with less capacity can improve the results, for example simply using a GlobalAveragePooling layer and feeding that directly into the final classifier can yield better results. \n", + "\n", + "All together, this can help account for why using MobileNet as a feature extractor performed better overall than our fine tuning efforts. Fine tuning is a great tool, but it may not always yield the best results. In our case, starting with pretrained weights and then retraining the WHOLE network with a very slow learning rate earned the best overall performance.\n", + "\n", + "Remember to experiment with multiple tactics!\n", + "\n", + "Lets try one last experiment still, before we decide that fine-tuning from ImageNet to CIFAR100 on MobileNetV2 is not the way to go..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Discriminative Fine Tuning\n", + "\n", + "In addition to freezing a fixed set of layers, you can iteratively unfreeze more and more layers. That is: Train the last 10 layers for a few epochs, then train the final 20 layers for some more epochs, then the final 30 layers for a few more epochs, and so on.\n", + "\n", + "This is a reminder that it isn't just the model that needs careful engineering: the training process should be carefully crafted as well. In the below code samples we show two (very similar) examples of this discriminative fine tuning. Consider playing with the parameters below as well to achieve better performance. Notice I used a higher learning rate than in the most successful experiment above... perhaps a slower one will perform better!" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "model.save(os.path.join(save_directory, \"adam_train_all.h5\"))" + "def discriminative_fine_tuning(model, optimizer, layer_unfreeze_points, epochs_per_freeze_point, batch_size):\n", + " # All histories returned for more holistic visualiation after the fact.\n", + " all_histories = []\n", + " \n", + " # Intially freeze everything, we'll unfreeze layers iteratively as we train\n", + " for layer in model.layers:\n", + " layer.trainable = False\n", + " \n", + " # Caller specifies blocks of layers to unfreeze at once\n", + " for current_unfreeze_point in layer_unfreeze_points:\n", + " \n", + " # Unfreeze everything after the current freeze point\n", + " print(\"Unfreezing layers after: \", current_unfreeze_point)\n", + " for layer in model.layers[current_unfreeze_point:]:\n", + " layer.trainable = True\n", + " \n", + " # Must compile after freezing/unfreezing or the changes won't be applied\n", + " model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n", + " \n", + " # Train at each unfreeze point\n", + " history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs_per_freeze_point, validation_split=0.2, verbose=True)\n", + " all_histories.append(history)\n", + " \n", + " return all_histories, model\n", + "\n", + "\n", + "def plot_combined_histories(all_histories):\n", + " # Some code to plot all the histories at once...\n", + " acc = []\n", + " val_acc = []\n", + " loss = []\n", + " val_loss = []\n", + "\n", + " # Simply cobble together the individual histories\n", + " for history in all_histories:\n", + " acc.extend(history.history['accuracy'])\n", + " val_acc.extend(history.history['val_accuracy'])\n", + " loss.extend(history.history['loss'])\n", + " val_loss.extend(history.history['val_loss'])\n", + "\n", + " # And plot them the same way as the function at the top of this notebook.\n", + " figure = plt.figure()\n", + "\n", + " plt.subplot(1, 2, 1)\n", + " plt.plot(acc)\n", + " plt.plot(val_acc)\n", + " plt.title('model accuracy')\n", + " plt.ylabel('accuracy')\n", + " plt.xlabel('epoch')\n", + " plt.tight_layout()\n", + "\n", + " plt.subplot(1, 2, 2)\n", + " plt.plot(loss)\n", + " plt.plot(val_loss)\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.tight_layout()\n", + "\n", + " figure.tight_layout()\n", + " plt.show()" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unfreezing layers after: 154\n", + "Train on 40000 samples, validate on 10000 samples\n", + "40000/40000 [==============================] - 278s 7ms/sample - loss: 4.7438 - accuracy: 0.0161 - val_loss: 4.6045 - val_accuracy: 0.0218\n", + "Unfreezing layers after: 144\n", + "Train on 40000 samples, validate on 10000 samples\n", + "40000/40000 [==============================] - 296s 7ms/sample - loss: 4.4093 - accuracy: 0.0565 - val_loss: 4.3743 - val_accuracy: 0.0691\n", + "Unfreezing layers after: 126\n", + "Train on 40000 samples, validate on 10000 samples\n", + "40000/40000 [==============================] - 319s 8ms/sample - loss: 3.7325 - accuracy: 0.1787 - val_loss: 3.9526 - val_accuracy: 0.1269\n", + "Unfreezing layers after: 108\n", + "Train on 40000 samples, validate on 10000 samples\n", + "40000/40000 [==============================] - 390s 10ms/sample - loss: 2.8413 - accuracy: 0.3263 - val_loss: 3.7912 - val_accuracy: 0.1707\n", + "Unfreezing layers after: 91\n", + "Train on 40000 samples, validate on 10000 samples\n", + "40000/40000 [==============================] - 428s 11ms/sample - loss: 2.1938 - accuracy: 0.4420 - val_loss: 3.7392 - val_accuracy: 0.2026\n", + "Unfreezing layers after: 73\n", + "Train on 40000 samples, validate on 10000 samples\n", + "40000/40000 [==============================] - 452s 11ms/sample - loss: 1.7895 - accuracy: 0.5260 - val_loss: 3.4883 - val_accuracy: 0.2418\n", + "Unfreezing layers after: 55\n", + "Train on 40000 samples, validate on 10000 samples\n", + "40000/40000 [==============================] - 470s 12ms/sample - loss: 1.5125 - accuracy: 0.5845 - val_loss: 3.6581 - val_accuracy: 0.2494\n", + "Unfreezing layers after: 37\n", + "Train on 40000 samples, validate on 10000 samples\n", + "40000/40000 [==============================] - 528s 13ms/sample - loss: 1.3054 - accuracy: 0.6363 - val_loss: 3.5968 - val_accuracy: 0.2632\n", + "Unfreezing layers after: 19\n", + "Train on 40000 samples, validate on 10000 samples\n", + "40000/40000 [==============================] - 631s 16ms/sample - loss: 1.1472 - accuracy: 0.6774 - val_loss: 3.9943 - val_accuracy: 0.2098\n" + ] + } + ], + "source": [ + "from tensorflow.keras.optimizers import SGD\n", + "\n", + "base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3))\n", + "\n", + "# We'll use the same \"squeeze\" w/ dropout structure from above...\n", + "# We have to use the \"functional\" API now, which is why we're not using model.add\n", + "old_top = base_model.output\n", + "old_top = GlobalAveragePooling2D()(old_top)\n", + "old_top = Dense(units=640, activation='relu')(old_top)\n", + "old_top = Dropout(rate=0.4)(old_top)\n", + "old_top = Dense(units=320, activation='relu')(old_top)\n", + "old_top = Dropout(rate=0.2)(old_top)\n", + "new_top = Dense(NUM_CLASSES, activation='softmax')(old_top)\n", + "\n", + "model = Model(inputs=base_model.input, outputs=new_top)\n", + "\n", + "# These points were carefully chosen based on the model.\n", + "# Specifially, each convolutional bottleneck block is unfrozen as a whole\n", + "# Note: Descending order is required for the above funtion to work as expected\n", + "layer_unfreeze_points = [\n", + " 154, # First, just train the new top\n", + " 144, # The final conv, and the final full block\n", + " 126, # After this we're unfreezing 2 blocks at a time\n", + " 108,\n", + " 91,\n", + " 73,\n", + " 55,\n", + " 37,\n", + " 19\n", + "]\n", + "\n", + "# Note, 2x slower than the default learning rate\n", + "opt = SGD(learning_rate=0.005)\n", + "\n", + "# Note: Even at 1 epoch per unfreeze point, we're training for twice as many epochs as the above (5 vs 9)\n", + "all_histories, model = discriminative_fine_tuning(model, opt, layer_unfreeze_points, 1, 128)\n", + "model.save(os.path.join(save_directory, \"discriminative_full_one_epoch.h5\"))\n", + "plot_combined_histories(all_histories)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "Taken together, it looks like we're still learning, even if it's a bit erratic. It takes a long time to train, and we are still overfitting, even while we keep improving on the validation data... This would be a good time to apply data augmentation to synthetically increase our dataset. " + "# Possibly still too fast, possibly too much unfreezing, possibly fine tuning is not a great fit\n", + "# for this problem/setup (MobileNetV2 ImageNet -> CIFAR100)." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unfreezing layers after: 154\n", + "Train on 40000 samples, validate on 10000 samples\n", + "Epoch 1/2\n", + "40000/40000 [==============================] - 308s 8ms/sample - loss: 4.9493 - accuracy: 0.0099 - val_loss: 4.7340 - val_accuracy: 0.0086\n", + "Epoch 2/2\n", + "40000/40000 [==============================] - 282s 7ms/sample - loss: 4.7790 - accuracy: 0.0119 - val_loss: 4.6724 - val_accuracy: 0.0097\n", + "Unfreezing layers after: 144\n", + "Train on 40000 samples, validate on 10000 samples\n", + "Epoch 1/2\n", + "40000/40000 [==============================] - 287s 7ms/sample - loss: 4.6780 - accuracy: 0.0170 - val_loss: 4.6144 - val_accuracy: 0.0158\n", + "Epoch 2/2\n", + "40000/40000 [==============================] - 285s 7ms/sample - loss: 4.5771 - accuracy: 0.0238 - val_loss: 4.5702 - val_accuracy: 0.0250\n", + "Unfreezing layers after: 126\n", + "Train on 40000 samples, validate on 10000 samples\n", + "Epoch 1/2\n", + "40000/40000 [==============================] - 306s 8ms/sample - loss: 4.4815 - accuracy: 0.0367 - val_loss: 4.5234 - val_accuracy: 0.0403\n", + "Epoch 2/2\n", + "40000/40000 [==============================] - 304s 8ms/sample - loss: 4.3731 - accuracy: 0.0550 - val_loss: 4.4697 - val_accuracy: 0.0552\n", + "Unfreezing layers after: 108\n", + "Train on 40000 samples, validate on 10000 samples\n", + "Epoch 1/2\n", + "40000/40000 [==============================] - 368s 9ms/sample - loss: 4.2426 - accuracy: 0.0849 - val_loss: 4.4011 - val_accuracy: 0.0696\n", + "Epoch 2/2\n", + "40000/40000 [==============================] - 359s 9ms/sample - loss: 4.0776 - accuracy: 0.1172 - val_loss: 4.3162 - val_accuracy: 0.0765\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'plot_combined_histories' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_directory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"discriminative_half_two_epoch.h5\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0mplot_combined_histories\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_histories\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'plot_combined_histories' is not defined" + ] + } + ], + "source": [ + "# The same experiment with a lower capacity classifier\n", + "base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3))\n", + "\n", + "# We'll use the same \"squeeze\" w/ dropout structure from above...\n", + "# We have to use the \"functional\" API now, which is why we're not using model.add\n", + "old_top = base_model.output\n", + "old_top = GlobalAveragePooling2D()(old_top)\n", + "old_top = Dense(units=640, activation='relu')(old_top)\n", + "old_top = Dropout(rate=0.4)(old_top)\n", + "old_top = Dense(units=320, activation='relu')(old_top)\n", + "old_top = Dropout(rate=0.2)(old_top)\n", + "new_top = Dense(NUM_CLASSES, activation='softmax')(old_top)\n", + "\n", + "model = Model(inputs=base_model.input, outputs=new_top)\n", + "\n", + "# These points were carefully chosen based on the model.\n", + "# Specifially, each convolutional bottleneck block is unfrozen as a whole\n", + "# Note: Descending order is required for the above funtion to work as expected\n", + "layer_unfreeze_points = [\n", + " 154, # First, just train the new top\n", + " 144, # The final conv, and the final full block\n", + " 126, # After this we're unfreezing 2 blocks at a time\n", + " 108\n", + "]\n", + "\n", + "# Note, 10x slower than default\n", + "opt = SGD(learning_rate=0.001)\n", + "all_histories, model = discriminative_fine_tuning(model, opt, layer_unfreeze_points, 2, 128)\n", + "model.save(os.path.join(save_directory, \"discriminative_half_two_epoch.h5\"))\n", + "\n", + "plot_combined_histories(all_histories)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Ignore that error message... I didn't want to re-run the training after adding the function.\n", + "# This is part of the joy (and horror) of working in Jupyter notebooks.\n", + "\n", + "plot_combined_histories(all_histories)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Final Note: Discriminative Fine Tuning\n", + "This looks good enough to train more... We could try more epochs at each unfreezing stage and/or unfreezing more layers. This also seems to confirm our idea that a **slow learning rate** is critical to our success with this particular setup. (This tends to be true in transfer learning generally!) But note, this is 8 epochs to get to 11% training accuracy and 7.6% validation accuracy. Plus, it looks like it might be on the way to overfitting. \n", "\n", - "In addition to freezing a fixed set of layers, you can iteratively unfreeze more and more layers. That is: Train the last 10 layers for a few epochs, then train the final 20 layers for some more epochs, then the final 30 layers for a few more epochs, and so on. This strategy has also been combined with some success with slowly decreasing the learning rate — so when just the final 10 layers are being trained set the learning rate fairly high, then reduce it slightly when you unfreeze more layers. \n", + "Earlier when we unfroze all layers and trained after 8 epochs we ended up at ~29% training accuracy and 40% validation accuracy (!!) so this might also serve as further proof that fine tuning is not going to perform as well as just using the ImageNet weights as a starting point and re-training the whole network. \n", "\n", - "This is something you can experiment with in the exericise, and a reminder that it isn't just the model that needs careful engineering: the training process should be carefully crafted as well. " + "# One Last Experiment — Learning Rate Decay\n", + "\n", + "This is similar to the last experiment, except each time we unfreeze any more layers we slow the learning rate to 90% of whatever it was previously. We still iteratively unfreeze, but we start with a higher learning rate (.005) and decay it as time goes on. The idea is to reduce learning as we increase the parameter count to further avoid overfitting. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "model.save(save_directory + \"adam_train_all\")" + "def discriminative_fine_tuning_decay(model, layer_unfreeze_points, epochs_per_freeze_point, batch_size, lr=.005):\n", + " # All histories returned for more holistic visualiation after the fact.\n", + " all_histories = []\n", + " \n", + " # Intially freeze everything, we'll unfreeze layers iteratively as we train\n", + " for layer in model.layers:\n", + " layer.trainable = False\n", + " \n", + " # Caller specifies blocks of layers to unfreeze at once\n", + " for current_unfreeze_point in layer_unfreeze_points:\n", + " \n", + " # Unfreeze everything after the current freeze point\n", + " print(\"Unfreezing layers after: \", current_unfreeze_point)\n", + " for layer in model.layers[current_unfreeze_point:]:\n", + " layer.trainable = True\n", + " \n", + " # Must compile after freezing/unfreezing or the changes won't be applied\n", + " print(\"Learning Rate: \", lr)\n", + " optimizer = SGD(learning_rate=lr)\n", + " model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n", + " lr *= 0.90\n", + " \n", + " # Train at each unfreeze point\n", + " history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs_per_freeze_point, validation_split=0.2, verbose=True)\n", + " all_histories.append(history)\n", + " \n", + " return all_histories, model" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unfreezing layers after: 154\n", + "Learning Rate: 0.005\n", + "Train on 40000 samples, validate on 10000 samples\n", + "Epoch 1/2\n", + "40000/40000 [==============================] - 280s 7ms/sample - loss: 4.8195 - accuracy: 0.0119 - val_loss: 4.6026 - val_accuracy: 0.0176\n", + "Epoch 2/2\n", + "40000/40000 [==============================] - 266s 7ms/sample - loss: 4.5964 - accuracy: 0.0224 - val_loss: 4.5570 - val_accuracy: 0.0286\n", + "Unfreezing layers after: 144\n", + "Learning Rate: 0.0045000000000000005\n", + "Train on 40000 samples, validate on 10000 samples\n", + "Epoch 1/2\n", + "40000/40000 [==============================] - 290s 7ms/sample - loss: 4.4396 - accuracy: 0.0437 - val_loss: 4.4292 - val_accuracy: 0.0703\n", + "Epoch 2/2\n", + "40000/40000 [==============================] - 287s 7ms/sample - loss: 4.1271 - accuracy: 0.1052 - val_loss: 4.1667 - val_accuracy: 0.1077\n", + "Unfreezing layers after: 126\n", + "Learning Rate: 0.004050000000000001\n", + "Train on 40000 samples, validate on 10000 samples\n", + "Epoch 1/2\n", + "40000/40000 [==============================] - 314s 8ms/sample - loss: 3.6116 - accuracy: 0.1880 - val_loss: 3.8710 - val_accuracy: 0.1428\n", + "Epoch 2/2\n", + "40000/40000 [==============================] - 334s 8ms/sample - loss: 3.0796 - accuracy: 0.2720 - val_loss: 3.6252 - val_accuracy: 0.1833\n", + "Unfreezing layers after: 108\n", + "Learning Rate: 0.0036450000000000007\n", + "Train on 40000 samples, validate on 10000 samples\n", + "Epoch 1/2\n", + "40000/40000 [==============================] - 366s 9ms/sample - loss: 2.6724 - accuracy: 0.3419 - val_loss: 3.4793 - val_accuracy: 0.2098\n", + "Epoch 2/2\n", + "40000/40000 [==============================] - 343s 9ms/sample - loss: 2.3380 - accuracy: 0.4085 - val_loss: 3.4162 - val_accuracy: 0.2267\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3))\n", + "\n", + "old_top = base_model.output\n", + "old_top = GlobalAveragePooling2D()(old_top)\n", + "old_top = Dense(units=640, activation='relu')(old_top)\n", + "old_top = Dropout(rate=0.4)(old_top)\n", + "old_top = Dense(units=320, activation='relu')(old_top)\n", + "old_top = Dropout(rate=0.4)(old_top)\n", + "new_top = Dense(NUM_CLASSES, activation='softmax')(old_top)\n", + "\n", + "model = Model(inputs=base_model.input, outputs=new_top)\n", + "\n", + "# These points were carefully chosen based on the model.\n", + "# Specifially, each convolutional bottleneck block is unfrozen as a whole\n", + "# Note: Descending order is required for the above funtion to work as expected\n", + "layer_unfreeze_points = [\n", + " 154, \n", + " 144, \n", + " 126, \n", + " 108\n", + "]\n", + "\n", + "# Some changes here...\n", + "all_histories, model = discriminative_fine_tuning_decay(model, layer_unfreeze_points, 2, 128)\n", + "model.save(os.path.join(save_directory, \"discriminative_half_decay_two_epoch.h5\"))\n", + "plot_combined_histories(all_histories)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This looks fine, overall the tactic even looks promising. It might be worth experimenting with different learning rates and decay rates/strategies... but the results are still not as good as just retraining the whole network with a slow learning rate. Cest la vie. \n", + "\n", + "# A Reminder: Don't Over Generalize These Results!\n", + "\n", + "This is just one set of experiments with two datasets (CIFRA100 and ImageNet). The results we found here will not hold for all combinations of datasets — consider applying many of these in your own research and be flexible about what might work well or poorly!\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {